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PAPER Special Section on Corpus-Based Speech Technologies Acoustic Modeling of Speaking Sty

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PAPER Special Section on Corpus-Based Speech T echnologies

Acoustic Modeling of Speaking Styles and Emotional Expressions in HMM-Based Speech Synthesis

Junichi YAMAGISHI?a),Student Member,Koji ONISHI??,Nonmember,Takashi MASUKO???,

and Takao KOBAYASHI?b),Members

SUMMARY This paper describes the modeling of various emotional expressions and speaking styles in synthetic speech using HMM-based speech synthesis.We show two methods for modeling speaking styles and emotional expressions.In the?rst method called style-dependent model-ing,each speaking style and emotional expression is modeled individu-ally.In the second one called style-mixed modeling,each speaking style and emotional expression is treated as one of contexts as well as phonetic, prosodic,and linguistic features,and all speaking styles and emotional ex-pressions are modeled simultaneously by using a single acoustic model. We chose four styles of read speech—neutral,rough,joyful,and sad—and compared the above two modeling methods using these styles.The re-sults of subjective evaluation tests show that both modeling methods have almost the same accuracy,and that it is possible to synthesize speech with the speaking style and emotional expression similar to those of the target speech.In a test of classi?cation of styles in synthesized speech,more than 80%of speech samples generated using both the models were judged to be similar to the target styles.We also show that the style-mixed mod-eling method gives fewer output and duration distributions than the style-dependent modeling method.

key words:HMM-based speech synthesis,expressive speech synthesis, speaking style,emotional expression,acoustic modeling,decision tree 1.Introduction

Recent research on speech synthesis has focused on gener-ating emotional expressiveness and various speaking styles in synthesized speech.The latest text-to-speech synthe-sis systems based on a large corpus can produce natural-sounding speech;yet such systems cannot change voice quality,speaking style,and emotions of synthesized speech with maintaining its naturalness.One promising approach to overcoming this problem is an HMM-based text-to-speech (TTS)technique[1]in which the speech parameters of a speech unit such as the spectrum,fundamental frequency (F0),and phoneme duration are statistically modeled and generated by using HMMs.For example,with regard to generating various speakers’voice characteristics in a neu-trally read speech,the HMM-based TTS system can gen-erate synthetic speech which resembles an arbitrarily given Manuscript received June30,2004.

Manuscript revised October14,2004.

?The authors are with the Interdisciplinary Graduate School of Science and Engineering,Tokyo Institute of Technology, Yokohama-shi,226–8502Japan.

?Presently,with NEC Corporation.

??Presently,with Corporate Research&Development Center, Toshiba Corporation.

a)E-mail:junichi.yamagishi@ip.titech.ac.jp

b)E-mail:takao.kobayashi@ip.titech.ac.jp

DOI:10.1093/ietisy/e88–d.3.502target speaker’s voice by using a speaker adaptation tech-nique[2].

There are many approaches that can be used to add emotional expressiveness to,and produce various speaking styles in,synthetic speech[3],[4].However,in most ap-proaches,the emotional expression and speaking style of synthetic speech are controlled based on prosodic and other rules such as heuristic adjustments of the F0level and range, speech tempo,and loudness.As a consequence,these ap-proaches do not always make it possible to express emotions and speaking styles of all speakers in synthetic speech.

In this paper,we describe an alternative approach that enables expressing various emotions and/or speaking styles easily and e?ectively in synthetic speech by using an HMM-based speech synthesis framework.In the proposed ap-proach,speaking styles and emotional expressions are statis-tically modeled and generated without using heuristic rules to control the prosody and other speech parameters of syn-thesized speech.We describe two methods for modeling speaking styles and emotional expressions[5].In the?rst method,each speaking style and emotional expression is modeled individually.We refer to a set of the resulting mod-els as a style-dependent model and call this method style-dependent modeling.In the second method,each speaking style and emotional expression is treated as one of contexts as well as phonetic,prosodic,and linguistic features,and all speaking styles and emotional expressions are modeled simultaneously by using a single acoustic model.We re-fer to the resulting model as a style-mixed model and call this method style-mixed modeling.We compared these two modeling methods using four styles of read speech—neu-tral,rough,joyful,and sad.

This paper is organized as follows.Section2gives an overview of the HMM-based TTS system.Section3de-scribes techniques of acoustic modeling of various speaking styles and emotional expressions.Experimental conditions and the results of subjective experiments are described in Sect.4.Section5summarizes our?ndings.

2.HMM-Based Speech Synthesis

The basic structure of the speech synthesis system used in this study is the same as that of the conventional HMM-based speech synthesis system[1]except that labels for the target speaking styles and emotional expressions are given with the target text in the synthesis stage.

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503 In the training stage,phoneme HMMs are trained us-

ing a speech database that includes several speaking styles

and emotional expressions.Spectrum and F0are modeled

by multi-stream HMMs in which output distributions for

the spectral and F0parts are modeled using a continuous

probability distribution and a multi-space probability distri-

bution(MSD)[6],respectively.To model variations in the

spectrum and F0,we take into account phonetic,prosodic,

and linguistic contexts,such as phoneme identity contexts,

stress-related contexts,and locational contexts.Then,a

decision-tree-based context clustering technique[7],[8]is

applied separately to the spectral and F0parts of the context-

dependent phoneme HMMs.In the clustering technique,

a decision tree is automatically constructed based on the

MDL criterion.We then perform re-estimation processes

of the clustered context-dependent phoneme HMMs using

the Baum-Welch(EM)algorithm.Finally,state durations

are modeled by a multivariate Gaussian distribution[9],and

a state clustering technique is applied to the state duration

models.

In the synthesis stage,?rst,an arbitrarily given text

in a certain speaking style is transformed into a sequence

of context-dependent phoneme labels.Based on the label

sequence,a sentence HMM is constructed by concatenat-

ing context-dependent phoneme HMMs.From the sentence

HMM,spectral and F0parameter sequences are obtained

based on the ML criterion[10]in which phoneme durations

are determined using state duration distributions.Finally,by

using an MLSA?lter,speech is synthesized from the gener-

ated mel-cepstral and F0parameter sequences.

3.Modeling of Styles in HMM-Based Speech Synthesis

We developed two acoustic modeling methods—style-

dependent modeling and style-mixed modeling—to model

various speaking styles in HMM-based speech synthesis.In

the following,the term style refers to the speaking style in-

cluding emotional expression.

3.1Tree-Based Context Clustering

Before describing in detail the style modeling methods,we

will brie?y review the tree-based context clustering tech-

nique that uses the minimum description length(MDL)cri-

terion[8].

Let S0be the root node of a decision tree and

U(S1,S2,...,S M)be a model?de?ned for the leaf node set

{S1,S2,...,S M}.A Gaussian pdf,N m,which is obtained

by combining several Gaussian pdfs classi?ed into the node

S m,is assigned to each node S m.An example of a decision

tree for M=3is shown in Fig.1.

The description length of U is given by

D(U)=1

m=1

Γm

K+K log(2π)+log|Σm|

+KM log G+C,(1)

Fig.1A decision tree for M=3.

whereΓm is the state occupancy count at node S m,K is the

dimensionality of the data vector,Σm is the diagonal covari-

ance matrix of the Gaussian pdf at node S m,G=

m=1

Γm,

and C is the code length required for choosing the model

which is assumed here to be constant.

Suppose that node S m of model U is split into two

nodes,S mqy and S mqn,by using question q.Let U be the

model obtained by splitting the S m of model U by question

q.We de?ne the di?erence between the description lengths

before and after the splitting as follows:

δm(q)=D(U )?D(U)(2)

By using this di?erence,δm(q),we can automatically con-

struct a decision tree.The process of constructing a decision

tree is summarized below.

1.De?ne initial model U as U={S0}.

2.Find node S m in model U and question q which min-

imizeδm (q ).

3.Terminate ifδm (q )>0.Ifδm (q )≤0,stop the split-

ting of the nodes.

4.Split node S m by using question q and replace U with

the resultant node set.

5.Go to step2.

3.2Style Modeling Techniques

In this paper,we describe two methods for modeling speak-

ing styles and emotional expressions in HMM-based speech

synthesis.

In the style-dependent modeling method,each style is

modeled individually by using an acoustic model.The tree-

based context clustering technique described in Sect.3.1is

applied separately to each style’s acoustic model as shown

in Fig.2.Then a pseudo root node is added to the resulting

decision trees of each style to combine the models for all

styles into a single acoustic model.One of the advantages

?Here,a model is a set of leaf nodes of a decision tree.

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Fig.2Part of a constructed decision tree in style-dependent modeling.A pseudo root node is added

to decision trees of each style to combine models for all styles into a single acoustic model.

Fig.3Part of a constructed decision tree in style-mixed modeling.Styles are split by using style-

related questions as well as other contexts.

of this method is that we can easily add a new style by con-

structing an acoustic model for it and adding a path from the pseudo root node to the root node of the decision tree for the new style.

In the style-mixed modeling method,each style is treated as one of contexts,and the tree-based context clus-tering technique is applied to all styles at the same time.As a result,all styles are modeled by using a single acoustic model as shown in Fig.3.The styles are automatically split by using style-related questions as well as other contexts during the construction of a decision tree.For the purpose of distinguishing between di?erent styles,we put contextual labels on all phonemes in each sentence.In this method,it is not easy to add new styles because the whole acoustic model must be reconstructed.On the other hand,we ex-pect that the sharing of the parameters of similar Gaussian pdfs by several styles would improve the accuracy of these parameters in the Gaussian pdf and would lead to a more compact acoustic model.4.Experiments

4.1Speech Database

To compare the proposed modeling methods,we chose four styles of read speech—polite,rough/impolite,joyful,and sad—and constructed speech database[11],which were composed of503phonetically balanced sentences obtained from the ATR Japanese speech database.All the sentences were uttered by a male speaker,MMI,and a female speaker, FTY,in all the styles.Both the speakers are professional narrators.We also used speech samples uttered by the same speakers in a neutral style for reference purposes.

In the503phonetically balanced sentences,there are a number of sentences whose meaning may be unsuitable for several styles except for neutral style.Therefore,we ?rst evaluated whether the recorded speech samples were perceived by listeners as being uttered in the intended styles. Nine male subjects were presented with all503sentences uttered in each of the styles and then asked whether they

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505

Table1Evaluation of recorded speech samples in four styles.

(a)Male speaker,MMI.

Polite Rough Joyful Sad

503(100%)493(95%)499(98%)502(99%)

(b)Female speaker,FTY.

Polite Rough Joyful Sad

503(100%)498(99%)502(99%)502(99%)

Table2Classi?cation of styles in the recorded speech.

(a)Male speaker,MMI.

Recorded Classi?cation(%)

Speech Neutral Polite Rough Joyful Sad Other

Neutral50.742.4 3.50.00.7 2.8

Polite38.260.40.0 1.40.00.0

Rough 3.5 2.884.0 1.4 2.1 6.2

Joyful0.00.00.01000.00.0

Sad0.7 6.9 4.20.079.98.3

(b)Female speaker,FTY.

Recorded Classi?cation(%)

Speech Neutral Polite Rough Joyful Sad Other

Neutral52.143.10.70.7 3.50.0

Polite38.958.30.0 2.10.70.0

Rough0.70.098.60.00.70.0

Joyful 1.4 6.90.091.00.00.7

Sad0.00.00.0 1.498.60.0 perceived the speech samples as having been uttered in the intended styles.

Table1shows the number and percentage of sentences which were perceived as having been uttered in the intended style by at least?ve subjects.In the table,(a)shows the results for the male speaker,MMI,and(b)shows the results for the female speaker,FTY.It can be seen that almost all of the speech samples in the databases were perceived as having been uttered in the intended styles by a majority of the subjects.

We then conducted a subjective evaluation test to clas-sify the speech samples of the recorded speech into?ve groups depending on the style of speech.Nine male subjects were asked to assign eight test sentences chosen at random from53test sentences to a neutral,polite,rough,joyful,or sad group.Speech samples that were not put by the subjects into one of these groups were classi?ed as“other”.

Table2shows the classi?cation results for the recorded speech.In the table,(a)shows the results for the male speaker,MMI,and(b)shows the results for the female speaker,FTY.These results show that for the rough,joyful, and sad styles,most of the speech samples were perceived by the subjects as having been uttered in the intended styles. However,the speech samples in the neutral and polite styles

Table3Phonemes used in the experiments.

V oiced Unvoiced

V owel a i u e o A I U E O

Plosive b by d dy g gy k ky p py f t

Fricative z j h hy s sh

A?ricate ts ch

Liquid r ry

Nasal m N my n ny

Semivowel y w

Double consonant cl

Silence/Pause sil pau

were perceived as being very similar.We therefore excluded the polite-style speech samples from the following experi-ments.

4.2Experimental Conditions

We used42phonemes including silence and pause as shown in Table3and took the following phonetic and linguistic contexts into account:

?the number of morae in a sentence;

?the position of the breath group in a sentence;

?the number of morae in the{preceding,current,and succeeding}breath groups;

?the position of the current accentual phrase in the cur-rent breath group;

?the number of morae and the type of accent in the {preceding,current,and succeeding}accentual phrases;?the part of speech of the{preceding,current,and suc-ceeding}morphemes;

?the position of the current mora in the current accentual phrase;

?the di?erences between the position of the current mora and the type of accent;

?{preceding,current,and succeeding}phonemes;?style(for style-mixed modeling only).

It is noted that these contexts except for the style are the same as those of the conventional HMM-based speech syn-thesis system[1],in which only the neutral style was taken into account.

Speech signals were sampled at a rate of16kHz and they were windowed by using a25-ms Blackman window with a5-ms shift.Then,mel-cepstral coe?cients were obtained by mel-cepstral analysis[12].Fundamental fre-quency was extracted using the ESPS get F0program[13]. The feature vectors consisted of25mel-cepstral coe?cients including the zeroth coe?cient,the logarithm of the fun-damental frequency,and their delta and delta-delta coe?-cients.We used5-state left-to-right HMMs.Both the style-dependent and style-mixed models were trained using450 sentences for each style.

Tables4and5show the number of distributions in each

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Table4The number of distributions before tree-based context cluster-ing.

(a)Male speaker,MMI.

Style-dependent Style-

Neutral Rough Joyful Sad Total mixed Spec.

F027126270532716427485108828108828

Dur

(b)Female speaker,FTY.

Style-dependent Style-

Neutral Rough Joyful Sad Total mixed Spec.

F027137265232694227054107656107656

Dur

Table5The number of distributions after tree-based context clustering using the MDL criterion.

(a)Male speaker,MMI.

Style-dependent Style-

Neutral Rough Joyful Sad Total mixed Spec.89175280892633772796

F0131612691368148354364404

Dur.10701272105795043493182

(b)Female speaker,FTY.

Style-dependent Style-

Neutral Rough Joyful Sad Total mixed Spec.69863573568027482269

F0146415451343124956014598

Dur.103314071531110550763801

model before and after decision-tree-based context cluster-ing using the MDL criterion,respectively.In the tables,(a) shows the results for the male speaker,MMI,and(b)shows that the results for the female speaker,FTY.The entries for the style-dependent and style-mixed columns in the tables show the number of distributions in the style-dependent and style-mixed models,respectively;the entries for the neutral, rough,joyful,and sad columns show the number of distri-butions for each style in the style-dependent model.The abbreviations Spec.,F0,and Dur.refer to the spectrum,F0, and state duration,respectively.Before the decision-tree-based context clustering,the context-dependent HMMs in the models have the same number of distributions for the spectrum,F0,and state duration;the style-dependent and style-mixed models also have the same number of distri-butions.From these tables,it can be seen that the num-ber of output and duration distributions in the style-mixed model was smaller than in the style-dependent model.This is because similar model parameters among some styles are shared and the number of redundant distributions decreased in the style-mixed model.Figures2and3show parts of the constructed decision trees for the F0part in the second state of the HMMs of the style-dependent and style-mixed Table6Subjective evaluation of reproduced styles of the male speaker, MMI.

(a)Style-Dependent Model.

Synthetic Classi?cation(%)

Speech Neutral Rough Joyful Sad Other

Neutral98.30.60.00.0 1.1

Rough 6.982.30.00.010.8

Joyful 1.10.094.90.0 4.0

Sad0.6 1.10.094.9 3.4

(b)Style-Mixed Model.

Synthetic Classi?cation(%)

Speech Neutral Rough Joyful Sad Other

Neutral98.90.00.00.0 1.1

Rough 2.889.80.0 1.1 6.3

Joyful0.60.096.00.0 3.4

Sad0.00.60.096.0 3.4

(c)Recorded Speech.

Recorded Classi?cation(%)

Speech Neutral Rough Joyful Sad Other

Neutral96.6 2.20.60.60.0

Rough 2.296.00.60.60.6

Joyful 1.1 1.197.80.00.0

Sad0.00.60.099.40.0 models,respectively.

4.3Subjective Evaluations of Styles in Synthesized

Speech

We conducted a subjective evaluation test to classify the styles of synthesized speech.For comparison,we also conducted a classi?cation test using the recorded speech. Eleven male subjects were asked to classify eight test sen-tences chosen at random from53test sentences not included in the training data as being neutral,rough,joyful,or sad de-pending on the style of speech?.Speech samples that were not assigned by the subjects to one of these groups were classi?ed as“other”.

Tables6and7show the classi?cation results for the synthesized and recorded speech for the male speaker,MMI, and female speaker,FTY,respectively.In the tables,(a) shows the results for the style-dependent model,(b)shows the results for the style-mixed model,and(c)shows the re-sults for the recorded speech.It can be seen from the results that both the modeling methods had almost the same repro-duction performance,and that we could synthesize speech in styles similar to those of the recorded speech.In these experiments,more than80%of speech samples generated using both models were judged to be similar to those in the ?Several speech samples used in the test are available at http://www.kbys.ip.titech.ac.jp/research/demo/.

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Table7Subjective evaluation of reproduced styles of the female speaker,FTY.

(a)Style-Dependent Model.

Synthetic Classi?cation(%)

Speech Neutral Rough Joyful Sad Other

Neutral92.5 1.9 5.00.00.6

Rough 3.185.6 1.39.40.6

Joyful8.80.090.60.00.6

Sad 3.8 6.90.088.70.6

(b)Style-Mixed Model.

Synthetic Classi?cation(%)

Speech Neutral Rough Joyful Sad Other

Neutral90.0 1.97.50.60.0

Rough0.690.00.08.1 1.3

Joyful 3.1 1.992.50.0 2.5

Sad 1.3 5.60.091.8 1.3

(c)Recorded Speech.

Recorded Classi?cation(%)

Speech Neutral Rough Joyful Sad Other

Neutral97.20.0 2.80.00.0

Rough0.098.90.0 1.10.0

Joyful 4.00.096.00.00.0

Sad 1.7 1.10.096.60.6 target styles.Note that the subjects,the test speech sam-ples presented to each subject,and the number of styles used were not the same as in the test described in Sect.4.1.As a result,some di?erences were shown in the classi?cation scores for the recorded speech in Table6(c)compared to those shown in Table2(a).

4.4Subjective Evaluations of Naturalness

We conducted a subjective evaluation test to rate the natural-ness of the speech synthesized by using the style-dependent model.Ten subjects listened to eight sentences chosen ran-domly from53test sentences and then they rated the natu-ralness of the synthesized speech.A3-point scale was used with3for“good”,2for“acceptable”,and1for“bad”.

Figure4shows the results of the rating test.In the?g-ure,(a)shows the results for the male speaker,MMI,and (b)shows the results for the female speaker,FTY.The scores shown in the?gure are the results for the synthe-sized speech in neutral,rough,joyful and sad styles,re-spectively.From these results,we can see that this model-ing method could generate the synthesized speech with rel-atively good naturalness in neutral,joyful,and sad styles. However,the scores for the rough-style speech samples are relatively lower than for the samples in the other styles.This is because the phoneme boundaries are unclear in rough-style speech

samples.

(a)Male speaker,

MMI.

(b)Female speaker,FTY.

Fig.4Subjective evaluation of naturalness of speech synthesized using style-dependent

modeling.

Fig.5Paired comparison test to assess the naturalness of synthesized speech generated using the style-dependent and style-mixed models for the male speaker,MMI.

Finally,we compared the naturalness of the synthe-sized speech generated by the style-dependent and style-mixed models for the male speaker,MMI,by using a paired comparison test.Sixteen male subjects were presented,in random order,with a pair of same-style speech samples syn-thesized using the two models,and then they were asked which synthesized speech sounded more natural.For each subject,four test sentences were chosen at random from53 test sentences not included in the training data.

Figure5shows the preference scores.It can be seen from the?gure that the naturalness of the speech samples synthesized using the two methods was almost the same, although the number of output and duration distributions in the style-mixed model was smaller than in the style-dependent model.From this result,we can conclude that style-mixed modeling is more e?ective for modeling speech in di?erent styles than the style-dependent modeling.

5.Conclusions

We have presented an approach to realizing various speak-ing styles and emotional expressions in synthetic speech us-

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ing HMM-based speech synthesis.We have developed two methods for modeling speaking styles and emotional ex-pressions—the style-dependent modeling and style-mixed modeling.We have shown that the two modeling methods have almost the same performance in the subjective eval-uation tests,and that it is possible to synthesize speech with speaking styles and emotional expressions similar to those of the recorded speech.In addition,it is also shown that the style-mixed modeling method can give fewer output and duration distributions than the style-dependent model-ing method.

Future work will focus on evaluating the proposed style modeling techniques by using di?erent styles and training conditions used in this study and developing variability of speaking styles and emotional expressions using style adap-tation and style interpolation techniques.

Acknowledgments

Authors would like to thank Prof.Keiichi Tokuda,Nagoya Institute of Technology for his valuable discussions.

A part of this work was supported by MEXT Grant-in-Aid for Scienti?c Research on Priority Areas746,JSPS Grant-in-Aid for Scienti?c Research(B)15300055,and JSPS Research Fellowships for Young Scientists164633. References

[1]T.Yoshimura,K.Tokuda,T.Masuko,T.Kobayashi,and T.

Kitamura,“Simultaneous modeling of spectrum,pitch and duration in HMM-based speech synthesis,”Proc.EUROSPEECH-99,vol.5, pp.2347–2350,Budapest,Hungary,Sept.1999.

[2]M.Tamura,T.Masuko,K.Tokuda,and T.Kobayashi,“Text-to-

speech synthesis with arbitrary speaker’s voice from average voice,”

Proc.EUROSPEECH2001,vol.1,pp.345–348,Aalborg,Denmark, Sept.2001.

[3]M.Schr¨o der,“Emotional speech synthesis:A review,”Proc.EU-

ROSPEECH2001,vol.1,pp.561–564,Aalborg,Denmark,Sept.

2001.

[4]M.Abe,“Speaking styles:Statistical analysis and synthesis by a

text-to-speech system,”in Progress in Speech Synthesis,eds.J.P.H.

van Santen,R.W.Sproat,J.P.Olive,and J.Hirschberg,pp.495–510, Springer,New York,1997.

[5]J.Yamagishi,K.Onishi,T.Masuko,and T.Kobayashi,“Modeling of

various speaking styles and emotions for HMM-based speech syn-thesis,”Proc.EUROSPEECH2003,vol.3,pp.2461–2464,Geneva, Switzerland,Sept.2003.

[6]K.Tokuda,T.Masuko,N.Miyazaki,and T.Kobayashi,“Hid-

den Markov models based on multi-space probability distribution for pitch pattern modeling,”Proc.ICASSP-99,vol.1,pp.229–232, Phoenix,USA,March1999.

[7]S.J.Young,J.Odell,and P.Woodland,“Tree-based state tying for

high accuracy acoustic modeling,”Proc.ARPA Human Language Technology Workshop,pp.307–312,New Jersey,USA,March1994.

[8]K.Shinoda and T.Watanabe,“MDL-based context-dependent sub-

word modeling for speech recognition,”J.Acoust.Soc.Jpn.(E), vol.21,no.2,pp.79–86,March2000.

[9]T.Yoshimura,K.Tokuda,T.Masuko,T.Kobayashi,and T.

Kitamura,“Duration modeling for HMM-based speech synthesis,”

Proc.ICSLP-98,pp.29–32,Sydney,Australia,Dec.1998.

[10]K.Tokuda,T.Kobayashi,and S.Imai,“Speech parameter genera-

tion from HMM using dynamic features,”Proc.ICASSP-95,vol.1,

pp.660–663,Madrid,Spain,May1995.

[11]Speaking Style&Emotional Speech Database(SS2003)in Prosodic

Corpus,Scienti?c Research of Priority Areas“Prosody and Speech Processing,”2003.

[12]K.Tokuda,T.Kobayashi,T.Fukada,H.Saito,and S.Imai,“Spectral

estimation of speech based on mel-cepstral representation,”IEICE Trans.Fundamentals(Japanese Edition),vol.J74-A,no.8,pp.1240–1248,Aug.1991.

[13]“ESPS Programs Version5.0,”Entropic Research Laboratory,

1993.

Junichi Yamagishi received the B.E.degree

in electrical and electronic engineering,M.E.

degree in information processing from Tokyo

Institute of Technology,Tokyo,Japan,in2002

and2003,respectively.He is currently a Ph.D.

student of the Interdisciplinary Graduate School

of Science and Engineering,Tokyo Institute of

Technology,Yokohama,Japan,and a research

fellow of the Japan Society for the Promotion

of Science(JSPS).His research interests include

speech synthesis,speech analysis,and speech recognition.He is a member of IEEE,ISCA and ASJ.

Koji Onishi received the B.E.degree in electrical and electronic en-gineering,M.E.degree in information processing from Tokyo Institute of Technology,Tokyo,Japan,in2001and2003,respectively.He is currently with NEC Corporation,Tokyo,

Japan.

Takashi Masuko received the B.E.de-

gree in computer science,M.E.degree in intelli-

gence science,and Dr.Eng.degree in informa-

tion processing from Tokyo Institute of Tech-

nology,Tokyo,Japan,in1993,1995,and2003,

respectively.In1995,he joined the Precision

and Intelligence Laboratory,Tokyo Institute of

Technology as a Research Associate.He is cur-

rently a Research Associate at the Interdisci-

plinary Graduate School of Science and Engi-

neering,Tokyo Institute of Technology,Yoko-hama,Japan.He is a co-recipient of both the Best Paper Award and the Inose Award from the IEICE in2001.His research interests include speech synthesis,speech recognition,speech coding,and multimodal interface. He is a member of IEEE,ISCA and ASJ.He is currently a researcher with Toshiba Corporation,Tokyo,Japan.

YAMAGISHI et al.:ACOUSTIC MODELING OF SPEAKING STYLES AND EMOTIONAL EXPRESSIONS

509 Takao Kobayashi received the B.E.degree

in electrical engineering,the M.E.and Dr.Eng.

degrees in information processing from Tokyo

Institute of Technology,Tokyo,Japan,in1977,

1979,and1982,respectively.In1982,he joined

the Research Laboratory of Precision Machin-

ery and Electronics,Tokyo Institute of Technol-

ogy as a Research Associate.He became an

Associate Professor at the same Laboratory in

1989.He is currently a Professor of the In-

terdisciplinary Graduate School of Science and

Engineering,Tokyo Institute of Technology,Yokohama,Japan.He is a

co-recipient of both the Best Paper Award and the Inose Award from the

IEICE in2001,and the TELECOM System Technology Prize from the

Telecommunications Advancement Foundation Award,Japan,in2001.His

research interests include speech analysis and synthesis,speech coding,

speech recognition,and multimodal interface.He is a member of IEEE,

ISCA,IPSJ and ASJ.

小学古诗试题和答案

小学古诗试题和答案二、春回大地（在扩号内填上带“春”字的词语）。 1、竹外桃花三两枝，（春江）水暖鸭先知。 2、（春潮）带雨晚来急，野渡无人舟自横。 3、（春色）满园关不住，一枝红杏出墙来。 4、（春风）又绿江南岸，明月何时照我还？三、数字天地（在扩号内填上数字）。 1、（两）岸青山相对出，孤帆（一）片日边来。 2、死去元知（万）事空，但悲不见（九）州同。 3、（一）道残阳铺水中，（半）江瑟瑟（半）江红。 4、南朝（四百八十）寺，多少楼台烟雨中。四、古诗百花园:(填花名) 1、忽如一夜春风来，千树万树（梨花）开。 2、（桃花）潭水深千尺，不及汪伦送我情。 3、待到重阳日，还来就（菊花）。 4接天莲叶无穷碧，映日（荷花）别样红。 5借问酒家何处有?牧童遥指（杏花）村。五、古诗七彩园:（填色彩） 1、山外（青）山楼外楼，西湖歌舞几时休? 2、等闲识得东风面，万（紫）千（红）总是春。 3、春风又（绿）江南岸，明月何时照我还? 4、（碧）玉妆成一树高，万条垂下（绿）丝绦。六、古诗动物园:（填动物） 1、小荷才露尖尖角，早有（蜻蜓）立上头。 2、江上往来人，但爱（鲈鱼）美。 3、两个（黄鹂）鸣翠柳，一行（白鹭）上青天。 4、两岸（猿）声啼不住，轻舟已过万重山。 5、竹外桃花三两枝，春江水暖（鸭）先知。 6、蒌蒿满地芦芽短，正是（河豚）欲上时。 7、柴门闻（犬）吠，风雪夜归人。 8、牧童骑（黄牛），歌声振林樾。七、把下面题目补充完整。 1 、《别董大》作者（高适）（唐）朝千里黄云白日曛，北风吹雁雪纷纷。___莫愁前路无知己____ ，__天下谁人不识君___ 。 2、《凉州词》作者（王翰）（唐）朝葡萄美酒夜光杯，欲饮琵琶马上催。____醉卧沙场君莫笑___ ，___古来征战几人回__ 。 3、《鹿柴》作者（王维）（唐）朝 __空山不见人___，____但闻人语响____ 。返景入深林，复照青苔上。 4、《送元二使安西》____渭城朝雨浥轻尘_____，____客舍青青柳色新___。___劝君更尽一杯酒_____，___西出阳关无故人____ 。这是（唐）诗人（王维）写的一首著名的送别诗。八、默写。

部编版七年级上册古诗词选择题及答案

部编版七年级上全册古诗词选择题及解析古诗词目录：第四课．古代诗歌四首一、观沧海/曹操二、闻王昌龄左迁龙标遥有此寄/李白三、次北固山下/王湾四、天净沙?秋思/马致远期中课外古诗词：五、峨眉山月歌/李白六、江南逢李龟年/杜甫七、行军九日思长安故园/岑参八、夜上受降城闻笛/李益期末课外古诗词：九、秋词（其一）/刘禹锡十、夜雨寄北/李商隐十一、十一月四日风雨大作（其二）/陆游十二、潼关/谭嗣同一、《观沧海》曹操 1、下面对这首诗的赏析，不恰当的一项是（） A．这首诗通过写作者在远征途中登上碣石山俯瞰大海所看见的壮观景象，展现了诗人宽广的胸襟。 B．诗歌前四联写诗人登上碣石山看见山岛耸立，树木茂盛，大海波澜壮阔的景象。 C．第五、六联通过丰富的想像，写出沧海之大，吞吐日月，含盈群星的气派。D．最后一句，如一曲雄壮的乐曲，在最激越处戛然而止，悲从中来，发出感慨。 2、下列对《观沧海》分析正确的一项是：（） A．“东临碣石，以观沧海”两句，交代了观海的地点，起得平稳而质朴。“临”字统领全篇，以下几句皆由此展开，具体写登山之所见所思。 B．“水何澹澹，山岛竦峙”两句是从俯视的角度总写看到的景象，接下来四句则由远及近动静结合地描绘了山岛的蓬勃生机和大海的苍茫辽阔。 C．诗中的两个“若”字，虚中有实，实中有虚，以奇特的想象，表现了大海吞吐日月星辰的气概，表达了诗人阔大的胸怀。 D．全诗借景抒情，情景交融。登临碣石山，诗人心潮澎湃，把眼前的景色、自己的想象同历史兴衰之感、忧国忧民之情巧妙地融会在一起。

3、对这首诗的理解不恰当的一项是（） A．开篇点题，交代了观察的方位、地点及观察的对象，“观”字统领全篇。B．“水何澹澹”描写了大海的近景，使人感到海边景色的壮丽多姿。 C．“洪波涌起”中的“涌”字，不仅让我们看到了大海波涌连天的形态，而且仿佛听到了惊涛拍岸的声音。 D．诗的最后两句“幸甚至哉，歌以咏志”是合乐时加上的，是诗的附文，跟诗的内容没有联系。【参考答案】 1、下面对这首诗的赏析，不恰当的一项是（ D ） A．这首诗通过写作者在远征途中登上碣石山俯瞰大海所看见的壮观景象，展现了诗人宽广的胸襟。 B．诗歌前四联写诗人登上碣石山看见山岛耸立，树木茂盛，大海波澜壮阔的景象。 C．第五、六联通过丰富的想像，写出沧海之大，吞吐日月，含盈群星的气派。D．最后一句，如一曲雄壮的乐曲，在最激越处戛然而止，悲从中来，发出感慨。【解析】D “悲从中来”是错误的，应该是“幸运极了，我唱这首歌来表达我的志愿”。 2、下列对《观沧海》分析正确的一项是：（ C ） A．“东临碣石，以观沧海”两句，交代了观海的地点，起得平稳而质朴。“临”字统领全篇，以下几句皆由此展开，具体写登山之所见所思。 B．“水何澹澹，山岛竦峙”两句是从俯视的角度总写看到的景象，接下来四句则由远及近动静结合地描绘了山岛的蓬勃生机和大海的苍茫辽阔。 C．诗中的两个“若”字，虚中有实，实中有虚，以奇特的想象，表现了大海吞吐日月星辰的气概，表达了诗人阔大的胸怀。 D．全诗借景抒情，情景交融。登临碣石山，诗人心潮澎湃，把眼前的景色、自己的想象同历史兴衰之感、忧国忧民之情巧妙地融会在一起。【解析】A．有误，应是“观”字统领全篇；B．有误，不是“由远及近”应是“由近及远”；D．有误，全诗借景抒情，情景交融。登临碣石山，诗人心潮澎湃，抒发了诗人博大的胸襟和建功立业的豪迈情怀而不是抒发历史兴衰之感、忧国忧民

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A．曹操B．李斯C．诸葛亮D．王安石 6．屈原《国殇》中用了比喻修辞手法的诗句是() A．车错毂兮短兵接B．旌蔽日兮敌若云 C．矢交坠兮士争先D．凌余阵兮躐余行 7．赞美兄弟皆有才识的词语是() A．难兄难弟B．人琴两亡C．阿大中郎D．三荆 8．君子食无求饱，居无求安，敏于事而()。 A．慎于言B．慎于思C．慎于行 9．“仁者见之谓之仁，知者见之谓之知”出自()。 A．《易经》B．《庄子》C．《老子》 10．《五柳先生传》中“不汲汲于富贵”的前一句是()。A．不汲汲于贫贱B．不戚戚于贫贱C．不郁郁于贫贱11．《牡丹亭》的作者是()。 A．关汉卿B．马致远C．汤显祖 12．“春潮带雨晚来急，野渡无人舟自横”出自()。 A．《滁州西涧》B．《山居秋暝》C．《春夜喜雨》 13．“居安思危，戒奢以俭”出自()的文章。 A．司马光B．魏征C．诸葛亮 14．孔子说：“质胜文则野，文胜质则史。()，然后君子。”A．文质相当B．文质兼备C文质彬彬 15．“己欲立而立人”的下句是()。

(完整版)中考语文古诗词赏析题选择题附答案(最新整理)

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上的波澜连成一片。 B、“几处早莺争暖树，谁家新燕啄春泥。”写诗人一路行来，偶尔可以见到早来的黄莺争着飞上向阳的枝头；不知是新到谁家的燕子衔了泥去筑巢。 C、“乱花渐欲迷人眼，浅草才能没马蹄。”写诗人看到路畔的野花已经开得五彩缤纷，使人眼花缭乱；矮矮的草地，骑马走过，马蹄刚能够被它埋没起来。 D、“最爱湖东行不足，绿杨阴里白沙堤。”写诗人来到绿杨成阴的白沙堤上，这里的景色更是美不胜收，看也看不够，于是赞叹道：这才是我最喜爱的地方！ 3、王湾《次北固山下》的分析理解不恰当的一项是（b） A、“客路青山外，行舟绿水前。”对偶，写“客路”、“行舟”，字里行间已有人在江南、神驰故里的飘泊之感，与尾联之“乡书”、“归雁”遥相呼应。 B、“潮平两岸阔，风正一帆悬。”“两岸阔”与“潮平”，“一帆悬”与“风正”均有因果关系。“风正”是风大之意。 C、“海日生残夜，江春入旧年。”表明诗人到江南正是冬末。诗人感到江南不光太阳来得早，好像春天也来得早。“海日生残夜，江春入旧年”两句历来脍灸人口。

小学语文古诗词练习题及答案

小学语文古诗词练习题及答案一、选择题。(每小题1分，共35分) 1、“少壮不努力，老大徒”出自《汉乐府●长歌行》。 A、悲伤 B、伤悲 C、忧伤 2、晏殊的《浣溪沙》中的“无可奈何花落去”的下句是。 A 、似曾相识鸟归来B、似曾相识雁归来C、似曾相识燕归来 3、《七步诗》的作者是。 A、曹操 B、曹丕 C、曹植 4、杜甫的《春夜喜雨》中的“晓看红湿处”的下句是。 A 、花重绵阳城B、花重锦州城C、花重锦官城 5、“春色满园关不住，一枝红杏出墙来。”出自叶绍翁的。 A 、《游园不植》B、《春望》C、《春夜喜雨》 6、“海内存知己，天涯若比邻。”是的诗句。 A 、王勃B、李白C、王维 7、“人生自古谁无死，留取丹心照。”是文天祥的诗句。 A 、汉青B、汗青C、汗清 8、“但使龙城飞将在，不教胡马度。”是王昌龄的诗句。 A 、阴山B、边关C、燕山 9、“停车坐爱枫林晚，霜叶红于二月花。”中的“坐”的意思是。 A 、因为B、坐下C、座位 10、杜牧的《江南春》中的“南朝四百八十寺”的下句是。 A、多少楼台烟波中 B、多少楼台风雨中 C、多少楼台烟雨中 11、“独在异乡为异客，每逢佳节倍思亲。”是的诗句。

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2、远眺洞庭，平静的湖面在皎洁的月光的辉映下，明亮而迷蒙，美丽的君山在其中，你不由吟道：“遥望洞庭山水色，白银盘里一青螺”。 3、中秋的夜晚，小芳在院子里对着一轮圆月，想着远在异乡的朋友，祈祷道：“但愿人长久，千里共婵娟”。 4、小明读了战争的资料，想起了以前那么艰苦的边塞生活，说道：“羌笛何须怨杨柳，春风不度玉门关。 5、毛毛读了古代清官们的故事，想到了那歌舞升平的迂腐生活，不禁吟起了《题临安邸》中的诗句：“暖风熏得游人醉，直把杭州作汴州”。 6、到瀑布脚下，昂首仰望，瀑布倾泻而下，撞击在岩石的棱角上溅起朵朵美丽的玉花。望着这美丽的瀑布，我不禁想起“飞流直下三千尺，疑是银河落九天”这句诗来。 7、梅，自古以来就倍受人们的称赞。“墙角数枝梅，凌寒独自开”便是一个例子。梅独自傲立于风雪中的顽强精神，确实令人感慨万千。 8、昨天下午，张老师布置了一道数学思考题。晚上。我绞尽脑汁，百思不得其解，就在我“山重水复疑无路，”时，爸爸走了过来。助我一臂之力，经他一点拨，我豁然开朗，真是“柳暗花明又一村”，于是迅速地解开了这道难题。 9、老师，“春蚕到死丝方尽，蜡炬成灰泪始干”，这诗句不是赞颂您燃烧自己，照亮别人的奉献精神，还能赞颂谁呢？ 10、古往今来，无数仁人志士为了祖国，抛头颅洒热血，文天祥说道：“人生自古谁无死，留取丹心照汗青”陆游临终前还告诫儿子：“王师北定中原日，家祭无忘告乃翁”王昌龄发出了“黄沙百战穿金甲，不破楼兰终不还”的满腔豪情。 11、月亮渐渐西沉，望着满天星星，我不由得想起了爸爸白天对我说的话，是啊，“少壮不努力，老大徒伤悲。爸爸，请你放心，我不会让你失望的。 12、、爷爷70大寿，亲朋好友都前来祝贺。大家祝爷爷“福如东海，寿比南山”，可爷爷却叹道：“夕阳无限好，只是近黄昏”。我赶紧把爷爷的话打住“老骥伏枥，志在千里。烈士暮年，壮心不已。爷爷，你的身子骨比年轻人还壮实。”爷爷笑了，摸摸我的头：“就你会耍贫嘴！” 13、赠别诗在我国古诗占重要地位，如李白的《赠汪伦》写到桃花潭水深千尺，不及汪伦送我情。王维的《送元二使安西》写到劝君更尽一杯酒，西出阳关无故人。高适的《别董大》写到莫愁前路无知己，天下谁人不识君。王昌龄的《芙蓉楼送辛渐》写到洛阳亲友如相问，一片冰心在玉壶。王勃的《送杜少府之任蜀州》写到海内存知己，天涯若比邻。 14、人们常用杜甫的“随风潜入夜，润物细无声”一句来形容老师对学生默默无闻的培育，潜移默化的熏陶。唐朝大诗人王昌龄用“洛阳亲友如相问，一片冰心在玉壶。”来表白自己坚贞的操守，光明磊落的品格。曹植质问哥哥苦苦相逼的诗句：“本是同根生，相煎何太急。”

一年级古诗词大赛知识(试题标准答案)

一年级古诗词大赛知识（试题答案）第一关：基础知识 1．江南可采莲，莲叶何（ )( )。田 2．鱼戏莲叶（），鱼戏莲叶东。间 3．（）（）园中葵，朝露待日晞。青 4．（）春布德泽，万物生光辉。阳 5．敕勒川，阴（）下。山 6．鹅，鹅，鹅，曲项向（）歌。天 7．过江千尺浪，入（）万竿斜。竹 8．碧玉妆成一树（），万条垂下绿丝绦。高 9．解落三秋叶，能开二月（）。花 10．不知细叶谁裁出，二月（）风似剪刀。春 11．少小离家老大回，乡（）无改鬓毛衰。音 12．黄河（）上白云间，一片孤城万仞山。远 13．白日依山尽，黄河（）海流。入 14．欲穷千里（），更上一层楼。目 15．春眠不觉晓，处处闻啼（）。鸟 1

16．夜来（）雨声，花落知多少。风17．葡萄（）酒夜光杯，欲饮琵琶马上催。美18．秦时（）月汉时关，万里长征人未还。明 19. 寒雨连（）夜入吴，平明送客楚山孤。江 20. 洛阳亲友如相问，一片冰心在（）壶。玉 21. 遥知兄弟登高处，遍插茱萸（）一人。少 22. 渭城朝雨浥轻尘，客舍（）青柳色新。青 23. 返景入深（），复照青苔上。林 24．独在异乡为异客，每逢佳节倍（）亲。思25．举头望明月，低头（）故乡。思26．日照香炉（）紫烟，遥看瀑布挂前川。生27．又疑瑶台镜，飞在（）云端。青28．凡是人，皆须爱，天（）覆，地同载。同29．才大者，望自大，人所服，（）言大。非 30. 凡取与，贵分晓，与宜（），取宜少。多 31．锄禾日当午，汗滴禾下（）。土32．松下（）童子，言师采药去。问2

33．泉眼无（）惜细流，树荫照水爱晴柔。声34．不解藏踪迹，浮萍一道（）。开35．（）看山有色，近听水无声。远36．谁知盘（）餐，粒粒皆辛苦。中第二关：能力提升 1．天苍苍，。野茫茫。 2．白毛浮绿水，。红掌拨清波。 3．父母呼，应勿缓，。父母命，行勿懒。 4.事虽小，勿擅为，，。苟擅为，子道亏。 5．物虽小，，苟私藏，。勿私藏，亲心伤 6．出必告，反必面，，。居有常，业无变。 3

古诗词题目及答案

古诗词试卷一一、选择题(每题两分，共计40分) 1、“少壮不努力，老大徒（）”出自《汉乐府●长歌行》。 A、悲伤 B、伤悲 C、忧伤 2、《七步诗》的作者是（）。 A、曹操 B、曹丕 C、曹植 3、杜甫的《春夜喜雨》中的“晓看红湿处”的下句是（）。 A 、花重绵阳城B、花重锦州城C、花重锦官城 4、“春色满园关不住，一枝红杏出墙来。”出自叶绍翁的（）。 A 、《游园不植》B、《春望》C、《春夜喜雨》 5、“海内存知己，天涯若比邻。”是（）的诗句。 A 、王勃B、李白C、王维 6、“人生自古谁无死，留取丹心照（）。”是文天祥的诗句。 A 、汉青B、汗青C、汗清 7、“但使龙城飞将在，不教胡马度（）。”是王昌龄的诗句。 A 、阴山B、边关C、燕山 8、“停车坐爱枫林晚，霜叶红于二月花。”中的“坐”的意思是（）。 A 、因为B、坐下C、座位 9、杜牧的《江南春》中的“南朝四百八十寺”的下句是（）。 A、多少楼台烟波中 B、多少楼台风雨中 C、多少楼台烟雨中 10、“独在异乡为异客，每逢佳节倍思亲。”是（）的诗句。 A 、王维B、王之涣C 、王勃 11、“野火烧不尽，春风吹又生。”出自（）。 A 白居易《赋得古原草送别》B、王昌龄《出塞》C、杜牧《江南春》 12、“忽如一夜春风来，千树万树梨花开。”写的是（）。 A 、春色B、梨花C、雪景 13、龚自珍的《己亥杂诗》”落红不是无情物，化着春泥更护花。“中的“红”指的是（）。 A、红色 B、红花 C、树叶

14、“海上生明月。（）共此时”是张九龄的诗句。 A 、天地B、天下C、天涯 15、“莫愁前路无知己，天下谁人不识君？”出自（）的《别董大》。 A、高适 B、王昌龄 C、王勃 16、“正是江南好风景，落花时节又逢君。”中的”君“指的是（）。 A 、李延年 B 、李龟年C、李贺 17、分别号称“诗仙”、“诗圣”、“诗鬼”的诗人是（）。 A、李贺、杜甫、李商隐 B、李白、李贺、杜甫 C、李白、杜甫、李贺 18、《山行》是描绘了（）的景色。 A、春天 B、夏天 C、秋天 D、冬天 19、“劝君更尽一杯酒，西出阳关无故人。”出自（）的名句。 A、李白 B、王维 C、王昌龄 D、杜牧 20、把“春风”比作“剪刀”的是哪首诗？（） A、《忆江南》 B、《滁州西涧》 C、《咏柳》 D、《游园不值》二、填空题（每题两分，共计16分） 1.小明成天心思不在学习上，请你用学过的诗句劝他：，。 2.当我们浪费粮食时，老爷爷经常用唐代李绅的诗局来教育我们：，。 3.有时候，有些人对自己所处的环境下正在做的事情反而不及旁人看得清楚，这就是人们常说的当局者迷，旁观者清。宋朝诗人苏轼在《题西林壁》中的诗句，说明的就是这个朴素的道理。 4.当我们要报答母亲的深恩时，我们会很自然地吟诵起唐代诗人孟郊的《游子吟》，。 5.当我们在外地过节时，常引用唐代诗人王维在《九月九日忆山东兄弟》中的，来表达对家人的怀念。

小升初古诗词试题及答案完整版

小升初古诗词试题及答案集团标准化办公室：[VV986T-J682P28-JP266L8-68PNN]

小升初古诗词试题及答案小升初古诗词试题及答案 1、在括号里填上带“春”的词语。 (1) (春蚕 )到死丝方尽，蜡炬成灰泪始干。 (2) (春潮 )带雨晚来急，野渡无人舟自横。 (3) (春江 )潮水连江平，海上明月共潮生。 (4) (春风 )又绿江南岸，明月何时照我还? (5) (春色 )满园关不住，一枝红杏出墙来。 (6) (春城 )无处不飞花，寒食东来御柳斜。 (7) (春宵 )一刻值千金，花有清香月有阴。 (8) 忽如一夜(春风 )来，千树万树梨花开。 2、在括号里填上动物或植物名。 (1) (孔雀)东南飞，五里一徘徊。 (2) 故人西辞(黄鹤 )楼，烟花三月下扬州。 (3) 西塞山前(白鹭 )飞，桃花流水(鳜鱼 )肥。 (4) 枯 (藤) 老（树）昏 (鸦)，小桥流水人家。 (5) 乱花渐欲迷人眼，浅(草)才能没(马 )蹄。 (6) 儿童急走追(黄蝶 )，飞入(菜花 )无处寻。 (7) 泥融飞(燕子)，沙暖睡(鸳鸯 )。 (8) 柴门闻(犬吠)吠，风雪夜归人。 (9) (竹)外(桃花 )三两枝，春江水暖(鸭 )先知。

(10)(小荷 )才露尖尖角，早有(蜻蜓)立上头。 (11) 童孙未解工耕织，也傍(桑 )阴学种(麻 )。 (12) 江晚正愁余，山深闻(鹧鸪)。 (13) 留恋戏(蝶)时时舞，自在娇(莺 )恰恰啼。 3、下列诗句写的是哪个季节诗题是什么作者是谁请填在括号里。季节诗题作者 ①月落乌啼霜满天，江枫渔火对愁眠。(秋 ) 《风桥夜泊》 (张继 ) ②碧玉妆成一树高，万条垂下绿丝绦。(春 ) 《咏柳》(贺知章) ③天间小雨润如酥，草色遥看近却无。(春 ) 《早春》(韩愈) ④千山鸟飞绝，万径人踪灭。 (冬 ) 《江雪》 (柳宗元) ⑤接天莲叶无穷碧，映日荷花别样红。(夏)《晓出静慈寺送林子方》(杨万里) ⑥墙角数枝梅，凌寒独自开。 (冬 ) 《梅花》 (王安石) ⑦借问酒家何处有，牧童遥指杏花村。(春 ) 《清明》(杜牧 )

小学古诗词试题(答案)复习过程

小学古诗词试题(答案)

小学古诗词积累竞赛试题一．名句联句。 1、儿童相见不相识，（笑问客从何处来）。 2、白发三千丈，（缘愁似个长）。 3、（小荷才露尖尖角），早有蜻蜓立上头。４、（不知细叶谁裁出），二月春风似剪刀。５、接天莲叶无穷碧，（映日荷花别样红）。６、（葡萄美酒夜光杯），欲饮琵琶马上催。７、柴门闻犬吠，（风雪夜归人）。８、（莫愁前路无知己），天下谁人不识君？９、（两岸青山相对出），孤帆一片日边来。１０、（夕阳无限好），只是近黄昏。１１、昼出耘田夜绩麻，（村庄儿女各当家）。１２、野旷天低树，（江清月近人）。１３、（春风又绿江南岸），明月何时照我还。１４、日出江花红胜火，（春来江水绿如蓝）。１５、停车坐爱枫林晚，（霜叶红于二月花）。１６、人有悲欢离合，（月有阴晴圆缺）。二．按古诗内容填空。 1.在A《登鹳鹤楼》、B《江上渔者》、C《望庐山瀑布》、D《塞下曲》这五首诗中，描写劳动人民的艰苦生活，反映作者对劳动人民同情的诗句是（B 君看一叶舟，出没风波里。）。常用来说明“站得高，望得远”的诗句是（A欲穷千里目，更上一层楼）。运用夸张手法描写的诗句是（C飞流直下三千尺，疑是银河落九天）。形容“箭法高超”的诗句是（D平明寻白羽，没在石棱中。）。２．新春佳节，一派喜庆的气氛，人们也异常喜悦，用各种方法来喜迎这个中国人的传统节日，如燃放鞭炮等，正如（王安石）所写的《元日》：爆竹声中一岁除，春风送暖入屠苏。千门万户曈曈日，总把新桃换旧符。

古诗选择题带答案详解

高考语文古诗鉴赏题中的选择题【试题一】阅读下面一首宋诗，完成第8－9题村行王禹偁马穿山径菊初黄，信马悠悠野兴长。万壑有声含晚籁，数峰无语立斜阳。棠梨叶落胭脂色，荞麦花开白雪香。何事吟余忽惆怅？村桥原树似吾乡。8．对这首诗的赏析，不恰当的一项是 A．首联照应题目，点明地点和时令，写出了诗人信马徐行、观赏山野景色的悠然兴致。B．第二联上下句构成对比，生动地表现出山中有时喧响有时静穆的景象。 C．第三联以"胭脂"和"白雪"为喻，形象地描绘出山村绚丽多彩的秋景。 D．最后两句设为问答，抒发了诗人由外界景物所触发的浓浓的思乡之情。 9．对这首诗的分析，最恰当的一项是 A.全诗抒发的是诗人观赏山野景色时悠然自得的心情。 B．诗的主旨是表达诗人对美丽幽静山村的由衷赞美与向往。 C．全诗情景交融，诗人思乡的惆怅心情已经渗透在前面的景物描写之中。 D．诗中通过情绪的陡然转折，表现了诗人深藏于内心、随时会被触发的怀乡之情。【试题二】16．阅读下面唐诗，完成①—②题。（6分）登鹳雀楼畅当迥临飞鸟上，高出世尘间。天势围平野，河流入断山。 [注]鹳雀楼：唐朝时建于今山西省永济县西南高阜上的一座三层楼，是当时的登临胜地，后废毁。 ①对这首诗的赏析，不恰当的两项是 A．诗人的视角在前后两联发生了转换，前一联写俯视所感，后一联写纵目所望。 B．前一联真实客观地写出了鹳雀楼耸入云天、飞鸟难及的巍巍高度。 C．后一联以天垂四野、黄河奔向远方山谷的壮阔景象，映衬出鹳雀楼的雄伟气势。 D．运用对偶，给人以工整匀称的美感，这是本诗写作上的一个突出特点。 E．全诗主旨是抒发诗人登上鹳雀楼凭高四望时胸中所涌起的壮志豪情。【试题三】17．阅读下面的唐诗，完成第①－②题。（6分）江楼旧感赵嘏独上江楼思渺然，月光如水水如天。同来望月人何处？风景依稀似去年。 ①对这首诗的赏析，不恰当的两项是 A．一二句情景交融，表达了诗人登临江楼、眺望江月时内心涌动的浩渺情思。

PAPER Special Section on Corpus-Based Speech Technologies Acoustic Modeling of Speaking Sty

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