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Adaptive Noise Reduction and Voice Activity Detection for improved Verbal Human-Robot Interaction

Adaptive Noise Reduction and Voice Activity Detection for improved Verbal Human-Robot Interaction
Adaptive Noise Reduction and Voice Activity Detection for improved Verbal Human-Robot Interaction

Adaptive Noise Reduction and Voice Activity Detection for improved Verbal Human-Robot Interaction using Binaural Data

Robert Brueckmann,Andrea Scheidig and Horst-Michael Gross

Abstract—Speech has become an important part in Human Robot Interaction(HRI),e.g.for person detection systems by using localized sound sources or for applications in Automatic Speech Recognition(ASR)systems.By using speech in HRI in real world environments,we have to deal with mostly high and varying background noise,reverberation and also with different sound sources superimposing speech and other noises.Therefore,for real world scenarios a suitable signal preprocessing is essential.

In this paper,we present a part of the arti?cial auditory system implemented on the mobile interaction robot H OROS using only two low cost microphones.We combined neural Voice Activity Detection(V AD)and adaptive noise reduction which are essential aspects for HRI using mobile robot systems in changing and populated real-world environments.

In the result,our system is able to robustly react on speech signals from its human interaction partner while ignoring other sound sources.Experiments show a signi?cantly improved ASR performance in demanding environments making the system suitable for the use in real-world scenarios.

I.INTRODUCTION

When interacting with a mobile robot,speech plays an important role as natural interface between human and a robot.It is therefore desirable for a mobile robot to robustly recognize speech in real world scenarios.Unfortunately,real world environments often lead to reverberation,and besides the sound source of interest,there may be other interfering sound sources present,like fans,noises produced by the robot itself,etc..In the consequence,the performance of most speech processing methods,e.g.speech detection,sound source localization,Automatic Speech Recognition(ASR), etc.,will be signi?cantly degraded.Hence,the preprocessing of the sound signals is necessary providing an improved input signal.Aspects that should be considered for the preprocessing of sound signals should integrate the detec-tion of different sound sources,the classi?cation of them as speech/non-speech,and signal enhancement by adaptive noise reduction.

Sound source localization and separation on mobile robots requires microphone arrays with at least two microphones. In our work,we focus on an implementation using only two low cost acoustic sensors.These provide the minimum hardware equipment to apply sound source separation and localization,although the accuracy of both aspects could be increased using more microphones.The robot BIRON[1] This work is partially supported by TMWFK-Grant#B509-03007to H.-M.Gross and HWP-Grant to A.Scheidig

R.Brueckmann,A.Scheidig,and H.-M.Gross are with the Neuroinforma-tics and Cognitive Robotics Lab,Technical University of Ilmenau,Germany andrea.scheidig@tu-ilmenau.de also uses two microphones for sound source localization and separation,but there is no V oice Activity Detection(V AD) used.Speech is assumed if other sensory cues detect further hypotheses of a human present at the direction of a detected sound source(https://www.wendangku.net/doc/104014243.html,ing face detection).Another robot using two microphones is ARMAR II[2]which implements V AD based on energy and zero-crossing-rate.

While it is basically possible to gather localization in-formation from only two microphones,there are several implementations using more microphones.Both the robots SIG2[3]and Spartacus[4]use eight microphones for source separation and speech recognition.In the result,Spartacus is able to track up to four sound sources simultaneously. Furthermore,the simultaneous voice of three speakers can be separated with this setup.

Speech/non-speech classi?cation of sound sources is also known as Speech Segmentation or V oice Activity Detecti-on(V AD).Using energy-based algorithms,one can detect speech and silence segments at high signal-to-noise ratios (SNR)[7].Speech is assumed if the signal level exceeds a threshold value,even if this is caused by a non-speech sound. Methods based on spectral entropy have been proposed to detect signal segments in noisy conditions[8],[10],[11]. These permit the detection even when the SNR is low.But both energy and spectral entropy based algorithms do not guarantee that the detected signal really contains speech. Other sounds(music,hand claps,closing doors,etc.)might be classi?ed as speech as well.Neural or statistical classi?-cation of speech and non-speech is able to distinguish these kinds of sounds from human speech.V AD based on neural networks using Multi-Layer-Perceptrons(MLP)and cepstral matrices as input is implemented in[12].This approach needs to be trained with noisy input signals of several background noises,and there is the need to train multiple MLPs,one for each type of background noise.Our approach uses adaptive noise reduction to pre-process the input signal of the V AD,therefore improved input signals can be used for training and there is no need to train multiple MLPs. Recurrent neural networks considering temporal aspects, requiring a more complex training than MLPs,have also been proposed for V AD[13],showing only slightly better classi?cation performance.

It is crucial for adaptive noise reduction algorithms to esti-mate the noise spectrum in order to apply spectral subtraction and to gather the original speech signal.Cohen[14]proposed the Minima Controlled Recursive Averaging(MCRA)tech-nique based on minimum statistics.This approach adapts its noise spectrum estimation and is therefore able to track the

2007 IEEE International Conference on

Robotics and Automation

Roma, Italy, 10-14 April 2007

ThA4.5

noise statistics even in non-stationary noisy environments. Rangachari[17]derived a similar algorithm yielding faster adaptation when the noise spectrum is rapidly changing.As we will discuss in Sec.III-C we extended this approach to use the output of a neural V AD to detect non-speech regions which further improves the noise suppression.

This paper is organized as follows.Sec.II describes the mobile robot H OROS on which the auditory system is implemented,and potential applications to the new auditory system are presented.Sec.III gives an overview of the auditory system itself and describes the integrated methods of source separation,voice activity detection,and adaptive noise reduction.Finally,in Sec.IV,we present experiments demonstrating the performance of the V AD and the adaptive noise reduction.

II.R OBOTIC P LATTFORM AND A PPLICATION

For our experiments we use the mobile interaction robot H OROS(HOme RObot System)1.H OROS’hardware platform is an extended Pioneer robot from ActiveMedia.It integrates an on-board PC(Pentium M, 1.6GHz,512MB)and is equipped with a laser-range-?nder(S ICK)and sonar sensors. For the purpose of Human Robot Interaction(HRI),this platform was mounted with different interaction-oriented modalities such as front and omnidirectional cameras,a touchscreen,and a speaker.

Two low-cost microphones of type YOGA EMR-106are mounted at both sides of the robot’s head.The distance between them is27cm.Since we are only using two microphones,we are able to use the on-board sound card of the Pioneer’s PC for audio recording,avoiding the need for additional multi-channel audio capture hardware.

Our proposed auditory system used on H OROS is designed for the following applications:

?ASR:By pre-processing the input sound signal of an ASR system,we expect lower word error rates.?Localization of speakers:By combining sound source localization and V AD we will differentiate between lo-calized speakers from other sounds,which is especially important for HRI.

?Recording of voice messages of users:The voice recor-der feature is used as a sort of answering machine.The user can provide voice messages which will be stored by the robot for later use.By using V AD,the beginning and the end of the user’s speech can be detected to automatically start and stop the recording.Thus,there is no user interaction required,e.g.by pressing start/stop buttons on the robot’s touch screen.

III.I NTEGRATED M ETHODS

The auditory system consists of several technical and methodical aspects,whereby the steps of audio signal pro-cessing are depicted in Fig.1.The input for the auditory system is provided by the raw stereo signal of the two 1http://wcms1.rz.tu-ilmenau.de/fakia/

HOROS-Homepage.horos_project.0.html?&L=1microphones,sampled at44.1kHz.Additionally,the sound localization and the people tracker[18]provide information on the direction of possible interaction partners currently speaking.This information is used to initialize delay-and-sum beamformers in the respective directions[6].The beam patterns of these beamformers are used by the Geometric Source Separation algorithm(see Sec.III-A).

The adaptive noise reduction uses a minimum statistics approach[19]to estimate the noise spectrum and to improve signal quality by applying a Wiener-type gain?lter(see Sec.III-C).The enhanced signal is used to detect speech using a neural voice activity detector(see Sec.III-B).The results of the V AD are used to further enhance the noise spectrum estimate,especially if no speech is present.

The respective parts of the auditory system will be de-scribed in the following sections,mainly focussing on the aspects of the V AD and adaptive noise reduction.

A.Source Separation

For many processing tasks,it is often desired to only process one of the captured sound sources,e.g.the desired speaker’s voice as input to the ASR system.Sound source separation techniques can be used to gather the sound source of interest out of an audio signal mixture recorded by spatially separated microphones.

We use the

Geometric Source Separation“(GSS)techni-que described in[5]and[6]for separation of the speaker’s voice and one interfering sound source from a different direction.The sound source localization(see Fig.1)is used to detect new sound sources and to initialize the beam patterns of the GSS algorithm accordingly with delay-and-sum beamformers[6].Additionally,our people tracker provides direction information on where speakers are to be expected,e.g.by detecting legs and skin color using other sensory cues.This information is used to select the desired GSS output channel containing the speech of the user for further processing.Although the attenuation using the two microphones is only1-2dB for typical noisy real-world recordings,the preprocessing provides a better signal than using only one microphone.

B.Voice Activity Detection

Distinguishing speech from other sound sources gives the robot a powerful new opportunity to detect potential interaction partners.We use a neural network approach for detecting speech in the surrounding of the robot.An MLP network is trained to classify short periods of the audio signal into speech or non-speech segments.

Mel Frequency Cepstral Coef?cients(MFCC)[9]are used for dimensionality reduction of the network’s input data and because of their ability to represent audio signals according to human perception.We use12MFCC as features for speech/non-speech classi?cation,excluding the?rst coef-?cient mostly representing the overall signal energy.To include information from previous frames,the differences between the last and the current coef?cients are calculated along with the change of these delta values.Additionally,we

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Fig.2.Classi?cation of a sound input consisting of three speech utterances (?rst 4sec.)and one non-speech noise signal (between 6th and 8th sec.)using different post-processing.Top:Speech segmentation using a ?xed threshold of 0.5.Middle:Speech segmentation using a state model ignoring weak classi?cation results close to zero.Bottom:Speech segmentation by averaging over 5frames of the neural network output and using the state model.

processing methods such as V AD and ASR.To improve the performance,we use a modi?ed version of the adaptive noise reduction proposed by Rangachari et al.[17].It enables the robot to operate in different noisy environments without manually collecting noise segments.The method is based on minimum statistics of the noisy input signal introduced by Martin [19].

A microphone signal containing additive noise can be expressed as

y (t )=s (t )+n (t )(3)where s (t )is the clean input signal and n (t )denotes the

additive noise component.To apply noise reduction to the re-corded noisy signal y (t ),it is necessary to obtain an estimate of the noise spectrum.Subsequently,spectral subtraction can be used to retrieve an estimate of the clean speech signal.Therefore,the noise reduction is applied in the frequency domain using a Wiener-type gain function G (k )resulting in

an estimate of the clean input signal ?S

(k )=G (k )Y (k ).The gain function is described by [17]

G (k )=

φs (k )

φs (k )+μφn (k )

(4)

where φs (k )is an estimate of the clean signal PDF and φn (k )is the PDF of the current noise spectrum estimate.

The oversubtraction factor μ≥1can be used for stronger attenuation if the segmental signal-to-noise ratio is very low,assuming there is currently only noise present [20].The gain function is used to attenuate the input signal by the amount of the estimated noise component.Therefore,its values are close to 0if there seems to be only noise currently present.If speech without noise is assumed,the values of G (k )are close to 1,leaving the input signal nearly unprocessed.The noise reduction method by Rangachari et al.[17]uses a signal-detection to determine which frequency bins are likely to contain speech components.The presence of speech at a speci?c frequency is assumed if there appears a sudden increase of the energy level in the respective frequency bin.In the consequence,the adaptation of the noise power spectrum estimate is inhibited not to contain the detected speech components.

Since the given approach does not differentiate between speech and other non-stationary sound sources,an extension with a V AD can reduce the error of the adapted noise power spectrum estimate.We propose to use the neural V AD presented in Sec.III-B for this purpose.The method by Rangachari provides a frequency dependent “speech presence probability”I (λ,k )at time frame λ.We use the output y (λ)∈[?1...+1]of the V AD to scale down the speech presence probability if a non-speech sound has been detected at the respective time frame.

I ?

(λ,k )=

y (λ)+1

2

·I (λ,k )if y (λ)

I (λ,k )otherwise

(5)

Since the use of the noisy microphone signal as input data lead to weak classi?cation results of the V AD,we propose a two-pass noise reduction to enhance the classi?cation performance.In a ?rst step,the method of Rangachari is applied using its original signal decision value I (λ,k ).The resulting enhanced audio signal is used as the input of the V AD.A second noise reduction with the same input data as the ?rst one is applied using the modi?ed speech presence probability I ?(λ,k )and providing the ?nal noise reduced audio signal.

IV.E XPERIMENTAL R ESULTS

Our proposed auditory system will be used for the appli-cations described in section II.Fig.3shows an example for the output of the auditory system integrating the aspects dis-cussed.The adaptive noise reduction reduces the underlying noise automatically providing better input signals to the V AD and ASR systems.Originally,the sound source localization responded to any sound source present.By using the V AD,the non-speech detections can be ignored providing the robot with a speaker localization.

The adaptive noise reduction decreases the in?uence of the noise automatically within the ?rst few seconds (see Fig.3).It is even capable of adapting to sudden changes in the underlying noise spectrum,as can be seen in Fig.4.Therefore,the V AD receives a signal containing much less

noise which improves the speech/non-speech classi?cation

performance.

Fig.3.Top:The noisy input signal containing two speech utterances and one non-speech sound (hand-clap).Middle:The output of the auditory system.The noise is reduced and the speech and non-speech segments have been classi?ed.Bottom:The detected angle of the sound source localization,which can be combined with the classi?cation results to ignore non-speech

sounds.

Fig.4.The wave form of one microphone signal (top)and the output of the proposed auditory system (bottom).The noise reduction reduces the amount of noise over time,even after a sudden change in the noise characteristics (dotted line).

The classi?cation performance of the neural V AD was evaluated using the voice of 30different speakers recorded in an of?ce environment at distances of 75cm and 150cm to the robot.These voice recordings were manually labeled as speech and non-speech segments.Additionally,non-speech sound sources were recorded such as hand claps,clicks,noises produced by computer-fans,etc..In total,there were 369,377frames processed by the V AD (~70min.at a window length of 1024samples and 50%overlap).Tab.I shows the resulting classi?cation rates:83,68%of the overall time frames were correctly classi?ed as speech/non-speech.Additionally,the voice recordings were grouped into recordings containing keywords of the ASR system and recordings of a read out text passage.As can be seen in Fig.5,the classi?cation rates of the keywords are slightly higher because these were articulated better,whereas the read out text contained more weak speech components.As can also be seen,the classi?cation performance is degrading

with larger distances.This is because the signal-to-noise ratio gets lower and the in?uence of reverberation is increasing in that case.The best classi?cation performance (87,23%)is achieved with ASR keywords at a distance of 75cm.Since the robot H OROS is a service robot aiming at dialog-based interaction,this is a typical use case.Most of the time the speaking user will be located right in front of the robot at interaction distance.

frames correct

speech 184,136152,699(82,93%)non-speech 185,241156,382(84,42%)overall

369,377

309,081(83,68%)

TABLE I

T HE TOTAL NUMBER OF FRAMES TESTED AND THE NUMBER OF

CORRECTLY CLASSIFIED FRAMES USING THE PROPOSED NEURAL

VAD.

Fig.6.Mean square error of the noise-power spectrum estimation of highly non-stationary noise sources using the method by Rangachari et al.[17]and the proposed extension with the neural V AD.

signals were used.The results are shown in Fig.7.As can be seen,the recognition performance can be increased signi?cantly in the presence of strong background noise.On recordings with an SNR of0dB,the ASR performance could be improved by37.5%.As expected,for high signal-to-noise ratios the improvement in terms of recognition rates gets smaller.The SNR of typical voice recordings presented to the robot in real-world scenarios is app.5-20dB.It depends on the sound level of the speaker and the respective environment noise.The increase of the recognition rate for this range of SNR was

1-28%.

Fig.7.Recognition rates of the ASR system at different signal-to-noise ratios.

V.C ONCLUSIONS

We presented a new adaptive auditory preprocessing for a mobile interaction robot.The system was designed to im-prove automatic speech recognition by using adaptive noise reduction.Experimental results showed a noticeable increase in word recognition performance of up to37.5%if the proposed auditory system was used.A voice activity detector has been implemented using a neural classi?er approach. This allows the robot to detect speakers by combining its result with a sound source localization.A voice recorder has been enhanced with an automatic start/stop controller reacting on the user’s speech.The system proved its ability to detect the user’s speech even under noisy conditions making it suitable for the use in real-world environments.

Further improvement of the system might be the imple-mentation of an improved sound source localization algo-rithm for detecting multiple sound sources simultaneously.

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古代晋灵公不君、齐晋鞌之战原文及译文

晋灵公不君(宣公二年) 原文: 晋灵公不君。厚敛以雕墙。从台上弹人,而观其辟丸也。宰夫胹熊蹯不熟,杀之,寘诸畚,使妇人载以过朝。赵盾、士季见其手,问其故而患之。将谏,士季曰:“谏而不入,则莫之继也。会请先,不入,则子继之。”三进及溜,而后视之,曰:“吾知所过矣,将改之。”稽首而对曰:“人谁无过?过而能改,善莫大焉。诗曰:‘靡不有初,鲜克有终。’夫如是,则能补过者鲜矣。君能有终,则社稷之固也,岂惟群臣赖之。又曰:‘衮职有阙,惟仲山甫补之。’能补过也。君能补过,衮不废矣。” 犹不改。宣子骤谏,公患之,使鉏麑贼之。晨往,寝门辟矣,盛服将朝。尚早,坐而假寐。麑退,叹而言曰:“不忘恭敬,民之主也。贼民之主,不忠;弃君之命,不信。有一于此,不如死也!”触槐而死。 秋九月,晋侯饮赵盾酒,伏甲将攻之。其右提弥明知之,趋登曰:“臣侍君宴,过三爵,非礼也。”遂扶以下。公嗾夫獒焉。明搏而杀之。盾曰:“弃人用犬,虽猛何为!”斗且出。提弥明死之。 初,宣子田于首山,舍于翳桑。见灵辄饿,问其病。曰:“不食三日矣!”食之,舍其半。问之,曰:“宦三年矣,未知母之存否。今近焉,请以遗之。”使尽之,而为之箪食与肉,寘诸橐以与之。既而与为公介,倒戟以御公徒,而免之。问何故,对曰:“翳桑之饿人也。”问其名居,不告而退。——遂自亡也。 乙丑,赵穿①攻灵公于桃园。宣子未出山而复。大史书曰:“赵盾弑其君。”以示于朝。宣子曰:“不然。”对曰:“子为正卿,亡不越竟,反不讨贼,非子而谁?”宣子曰:“呜呼!‘我之怀矣,自诒伊戚。’其我之谓矣。” 孔子曰:“董狐,古之良史也,书法不隐。赵宣子,古之良大夫也,为法受恶。惜也,越竞乃免。” 译文: 晋灵公不行君王之道。他向人民收取沉重的税赋以雕饰宫墙。他从高台上用弹弓弹人,然后观赏他们躲避弹丸的样子。他的厨子做熊掌,没有炖熟,晋灵公就把他杀了,把他的尸体装在草筐中,让宫女用车载着经过朝廷。赵盾和士季看到露出来的手臂,询问原由后感到很忧虑。他们准备向晋灵公进谏,士季说:“如果您去进谏而君王不听,那就没有人能够再接着进谏了。还请让我先来吧,不行的话,您再接着来。”士季往前走了三回,行了三回礼,一直到屋檐下,晋灵公才抬头看他。晋灵公说:“我知道我的过错了,我会改过的。”士季叩头回答道:“谁能没有过错呢?有过错而能改掉,这就是最大的善事了。《诗经》说:‘没有人向善没有一个开始的,但却很少有坚持到底的。’如果是这样,那么能弥补过失的人是很少的。您如能坚持向善,那么江山就稳固了,不只是大臣们有所依靠啊。

如何翻译古文

如何翻译古文 学习古代汉语,需要经常把古文译成现代汉语。因为古文今译的过程是加深理解和全面运用古汉语知识解决实际问题的过程,也是综合考察古代汉语水平的过程。学习古代汉语,应该重视古文翻译的训练。 古文翻译的要求一般归纳为信、达、雅三项。“信”是指译文要准确地反映原作的含义,避免曲解原文内容。“达”是指译文应该通顺、晓畅,符合现代汉语语法规范。“信”和“达”是紧密相关的。脱离了“信”而求“达”,不能称为翻译;只求“信”而不顾“达”,也不是好的译文。因此“信”和“达”是文言文翻译的基本要求。“雅”是指译文不仅准确、通顺,而且生动、优美,能再现原作的风格神韵。这是很高的要求,在目前学习阶段,我们只要能做到“信”和“达”就可以了。 做好古文翻译,重要的问题是准确地理解古文,这是翻译的基础。但翻译方法也很重要。这里主要谈谈翻译方法方面的问题。 一、直译和意译 直译和意译是古文今译的两大类型,也是两种不同的今译方法。 1.关于直译。所谓直译,是指紧扣原文,按原文的字词和句子进行对等翻译的今译方法。它要求忠实于原文,一丝不苟,确切表达原意,保持原文的本来面貌。例如: 原文:樊迟请学稼,子曰:“吾不如老农。”请学为圃。子曰:“吾不如老圃。”(《论语?子路》) 译文:樊迟请求学种庄稼。孔子道:“我不如老农民。”又请求学种菜蔬。孔子道:“我不如老菜农。”(杨伯峻《论语译注》) 原文:齐宣王问曰:“汤放桀,武王伐纣,有诸?”(《孟子?梁惠王下》) 译文:齐宣王问道:“商汤流放夏桀,武王讨伐殷纣,真有这回事吗?(杨伯峻《孟子译注》) 上面两段译文紧扣原文,字词落实,句法结构基本上与原文对等,属于直译。 但对直译又不能作简单化理解。由于古今汉语在文字、词汇、语法等方面的差异,今译时对原文作一些适当的调整,是必要的,并不破坏直译。例如: 原文:逐之,三周华不注。(《齐晋鞌之战》) 译文:〔晋军〕追赶齐军,围着华不注山绕了三圈。

齐晋鞌之战原文和译文

鞌之战选自《左传》又名《鞍之战》原文:楚癸酉,师陈于鞌(1)。邴夏御侯,逢丑父为右②。晋解张御克,郑丘缓为右(3)。侯日:“余姑翦灭此而朝食(4)”。不介马而驰之⑤。克伤于矢,流血及屦2 未尽∧6),曰:“余病矣(7)!”张侯曰:“自始合(8),而矢贯余手及肘(9),余折以御,左轮朱殷(10),岂敢言病吾子忍之!”缓曰:“自始合,苟有险,余必下推车,子岂_识之(11)然子病矣!”张侯曰:“师之耳目,在吾旗鼓,进退从之。此车一人殿之(12),可以集事(13),若之何其以病败君之大事也擐甲执兵(14),固即死也(15);病未及死,吾子勉之(16)!”左并辔(17) ,右援拐鼓(18)。马逸不能止(19),师从之,师败绩。逐之,三周华不注(20) 韩厥梦子舆谓己曰:“旦辟左右!”故中御而从齐侯。邴夏曰:“射其御者,君子也。”公曰:“谓之君子而射之,非礼也。”射其左,越于车下;射其右,毙于车中。綦毋张丧车,从韩厥,曰:“请寓乘。”从左右,皆肘之,使立于后。韩厥俛,定其右。逢丑父与公易位。将及华泉,骖絓于木而止。丑父寝于轏中,蛇出于其下,以肱击之,伤而匿之,故不能推车而及。韩厥执絷马前,再拜稽首,奉觞加璧以进,曰:“寡君使群臣为鲁、卫请,曰:‘无令舆师陷入君地。’下臣不幸,属当戎行,无所逃隐。且惧奔辟而忝两君,臣辱戎士,敢告不敏,摄官承乏。” 丑父使公下,如华泉取饮。郑周父御佐车,宛茷为右,载齐侯以免。韩厥献丑父,郤献子将戮之。呼曰:“自今无有代其君任患者,有一于此,将为戮乎”郤子曰:“人不难以死免其君,我戮之不祥。赦之,以劝事君者。”乃免之。译文1:在癸酉这天,双方的军队在鞌这个地方摆开了阵势。齐国一方是邴夏为齐侯赶车,逢丑父当车右。晋军一方是解张为主帅郤克赶车,郑丘缓当车右。齐侯说:“我姑且消灭了这些人再吃早饭。”不给马披甲就冲向了晋军。郤克被箭射伤,血流到了鞋上,但是仍不停止擂鼓继续指挥战斗。他说:“我受重伤了。”解张说:“从一开始接战,一只箭就射穿了我的手和肘,左边的车轮都被我的血染成了黑红色,我哪敢说受伤您忍着点吧!”郑丘缓说:“从一开始接战,如果遇到道路不平的地方,我必定(冒着生命危险)下去推车,您难道了解这些吗不过,您真是受重伤了。”daier 解张说:“军队的耳朵和眼睛,都集中在我们的战旗和鼓声,前进后退都要听从它。这辆车上还有一个人镇守住它,战事就可以成功。为什么为了伤痛而败坏国君的大事呢身披盔甲,手执武器,本来就是去走向死亡,伤痛还没到死的地步,您还是尽力而为吧。”一边说,一边用左手把右手的缰绳攥在一起,用空出的右手抓过郤克手中的鼓棰就擂起鼓来。(由于一手控马,)马飞快奔跑而不能停止,晋军队伍跟着指挥车冲上去,把齐军打得打败。晋军随即追赶齐军,三次围绕着华不注山奔跑。韩厥梦见他去世的父亲对他说:“明天早晨作战时要避开战车左边和右边的位置。”因此韩厥就站在中间担任赶车的来追赶齐侯的战车。邴夏说:“射那个赶车的,他是个君子。”齐侯说: “称他为君子却又去射他,这不合于礼。”daier 于是射车左,车左中箭掉下了车。又射右边的,车右也中箭倒在了车里。(晋军的)将军綦毋张损坏了自己的战车,跟在韩厥的车后说: “请允许我搭乗你的战车。”他上车后,无论是站在车的左边,还是站在车的右边,韩厥都用肘推他,让他站在自己身后——战车的中间。韩厥又低下头安定了一下受伤倒在车中的那位自己的车右。于是逢丑父和齐侯(乘韩厥低头之机)互相调换了位置。将要到达华泉时,齐侯战车的骖马被树木绊住而不能继续逃跑而停了下来。(头天晚上)逢丑父睡在栈车里,有一条蛇从他身子底下爬出来,他用小臂去打蛇,小臂受伤,但他(为了能当车右)隐瞒了这件事。由于这样,他不能用臂推车前进,因而被韩厥追上了。韩厥拿着拴马绳走到齐侯的马前,两次下拜并行稽首礼,捧着一杯酒并加上一块玉璧给齐侯送上去,

《鞌之战》阅读答案(附翻译)原文及翻译

《鞌之战》阅读答案(附翻译)原文及翻 译 鞌之战[1] 选自《左传成公二年(即公元前589年)》 【原文】 癸酉,师陈于鞌[2]。邴夏御齐侯[3],逢丑父为右[4]。晋解张御郤克,郑丘缓为右[5]。齐侯曰:余姑翦灭此而朝食[6]。不介马而驰之[7]。郤克伤于矢,流血及屦,未绝鼓音[8],曰:余病[9]矣!张侯[10]曰:自始合,而矢贯余手及肘[11],余折以御,左轮朱殷[12],岂敢言病。吾子[13]忍之!缓曰:自始合,苟有险[14],余必下推车,子岂识之[15]?然子病矣!张侯曰:师之耳目,在吾旗鼓,进退从之[16]。此车一人殿之[17],可以集事[18],若之何其以病败君之大事也[19]?擐甲执兵,固即死也[20]。病未及死,吾子勉之[21]!左并辔[22],右援枹而鼓[23],马逸不能止[24],师从之。齐师败绩[25]。逐之,三周华不注[26]。 【注释】 [1]鞌之战:春秋时期的著名战役之一。战争的实质是齐、晋争霸。由于齐侯骄傲轻敌,而晋军同仇敌忾、士气旺盛,战役以齐败晋胜而告终。鞌:通鞍,齐国地名,在今山东济南西北。 [2]癸酉:成公二年的六月十七日。师,指齐晋两国军队。陈,

列阵,摆开阵势。 [3]邴夏:齐国大夫。御,动词,驾车。御齐侯,给齐侯驾车。齐侯,齐国国君,指齐顷公。 [4]逢丑父:齐国大夫。右:车右。 [5]解张、郑丘缓:都是晋臣,郑丘是复姓。郤(x )克,晋国大夫,是这次战争中晋军的主帅。又称郤献子、郤子等。 [6]姑:副词,姑且。翦灭:消灭,灭掉。朝食:早饭。这里是吃早饭的意思。这句话是成语灭此朝食的出处。 [7]不介马:不给马披甲。介:甲。这里用作动词,披甲。驰之:驱马追击敌人。之:代词,指晋军。 [8] 未绝鼓音:鼓声不断。古代车战,主帅居中,亲掌旗鼓,指挥军队。兵以鼓进,击鼓是进军的号令。 [9] 病:负伤。 [10]张侯,即解张。张是字,侯是名,人名、字连用,先字后名。 [11]合:交战。贯:穿。肘:胳膊。 [12]朱:大红色。殷:深红色、黑红色。 [13]吾子:您,尊敬。比说子更亲切。 [14]苟:连词,表示假设。险:险阻,指难走的路。 [15]识:知道。之,代词,代苟有险,余必下推车这件事,可不译。 [16]师之耳目:军队的耳、目(指注意力)。在吾旗鼓:在我们

《鞌之战》阅读答案附翻译

《鞌之战》阅读答案(附翻译) 《鞌之战》阅读答案(附翻译) 鞌之战[1] 选自《左传·成公二年(即公元前589年)》 【原文】 癸酉,师陈于鞌[2]。邴夏御齐侯[3],逢丑父为右[4]。晋解张御郤克,郑丘缓为右[5]。齐侯曰:“余姑 翦灭此而朝食[6]。”不介马而驰之[7]。郤克伤于矢, 流血及屦,未绝鼓音[8],曰:“余病[9]矣!”张侯[10]曰:“自始合,而矢贯余手及肘[11],余折以御,左轮 朱殷[12],岂敢言病。吾子[13]忍之!”缓曰:“自始合,苟有险[14],余必下推车,子岂识之[15]?——然 子病矣!”张侯曰:“师之耳目,在吾旗鼓,进退从之[16]。此车一人殿之[17],可以集事[18],若之何其以 病败君之大事也[19]?擐甲执兵,固即死也[20]。病未 及死,吾子勉之[21]!”左并辔[22],右援枹而鼓[23],马逸不能止[24],师从之。齐师败绩[25]。逐之,三周 华不注[26]。 【注释】 [1]鞌之战:春秋时期的著名战役之一。战争的实质是齐、晋争霸。由于齐侯骄傲轻敌,而晋军同仇敌忾、 士气旺盛,战役以齐败晋胜而告终。鞌:通“鞍”,齐

国地名,在今山东济南西北。 [2]癸酉:成公二年的六月十七日。师,指齐晋两国军队。陈,列阵,摆开阵势。 [3]邴夏:齐国大夫。御,动词,驾车。御齐侯,给齐侯驾车。齐侯,齐国国君,指齐顷公。 [4]逢丑父:齐国大夫。右:车右。 [5]解张、郑丘缓:都是晋臣,“郑丘”是复姓。郤(xì)克,晋国大夫,是这次战争中晋军的主帅。又称郤献子、郤子等。 [6]姑:副词,姑且。翦灭:消灭,灭掉。朝食:早饭。这里是“吃早饭”的意思。这句话是成语“灭此朝食”的出处。 [7]不介马:不给马披甲。介:甲。这里用作动词,披甲。驰之:驱马追击敌人。之:代词,指晋军。 [8]未绝鼓音:鼓声不断。古代车战,主帅居中,亲掌旗鼓,指挥军队。“兵以鼓进”,击鼓是进军的号令。 [9]病:负伤。 [10]张侯,即解张。“张”是字,“侯”是名,人名、字连用,先字后名。 [11]合:交战。贯:穿。肘:胳膊。 [12]朱:大红色。殷:深红色、黑红色。 [13]吾子:您,尊敬。比说“子”更亲切。

左传《齐晋鞌之战》原文+翻译+注释

左传《齐晋鞌之战》原文+翻译+注释 楚癸酉,师陈于鞌(1)。邴夏御侯,逢丑父为右②。晋解张御克,郑丘缓 为右(3)。侯日:“余姑翦灭此而朝食(4)”。不介马而驰之⑤。克伤于矢, 流血及屦2未尽∧?6),曰:“余病矣(7)!”张侯曰:“自始合(8),而矢贯余手 及肘(9),余折以御,左轮朱殷(10),岂敢言病?吾子忍之!”缓曰:“自始合,苟有险,余必下推车,子岂_识之(11)?然子病矣!”张侯曰:“师之耳目,在 吾旗鼓,进退从之。此车一人殿之(12),可以集事(13),若之何其以病败君之大事也?擐甲执兵(14),固即死也(15);病未及死,吾子勉之(16)!”左并辔(17) ,右援拐?鼓(18)。马逸不能止(19),师从之,师败绩。逐之,三周华不注(20) 韩厥梦子舆谓己曰:“旦辟左右!”故中御而从齐侯。邴夏曰:“射其御者,君子也。”公曰:“谓之君子而射之,非礼也。”射其左,越于车下;射其右,毙于车中。綦毋张丧车,从韩厥,曰:“请寓乘。”从左右,皆肘之,使立于后。韩厥俛,定其右。逢丑父与公易位。将及华泉,骖絓于木而止。丑父寝于轏中,蛇出于其下,以肱击之,伤而匿之,故不能推车而及。韩厥执絷马前,再拜稽首,奉觞加璧以进,曰:“寡君使群臣为鲁、卫请,曰:‘无令舆师陷入君地。’下臣不幸,属当戎行,无所逃隐。且惧奔辟而忝两君,臣辱戎士,敢告不敏,摄官承乏。”丑父使公下,如华泉取饮。郑周父御佐车,宛茷为右,载齐侯以免。韩厥献丑父,郤献子将戮之。呼曰:“自今无有代其君任患者,有一于此,将为戮乎?”郤子曰:“人不难以死免其君,我戮之不祥。赦之,以劝事君者。”乃免之。 在癸酉这天,双方的军队在鞌这个地方摆开了阵势。齐国一方是邴夏为齐侯赶车,逢丑父当车右。晋军一方是解张为主帅郤克赶车,郑丘缓当车右。齐侯说:“我姑且消灭了这些人再吃早饭。”不给马披甲就冲向了晋军。郤克被箭射伤,血流到了鞋上,但是仍不停止擂鼓继续指挥战斗。他说:“我受重伤了。”解张说:“从一开始接战,一只箭就射穿了我的手和肘,左边的车轮都被我的血染成了黑红色,我哪敢说受伤?您忍着点吧!”郑丘缓说:“从一开始接战,如果遇到道路不平的地方,我必定(冒着生命危险)下去推车,您难道了解这些吗?不过,您真是受重伤了。”daier解张说:“军队的耳朵和眼睛,都集中在我们的战旗和鼓声,前进后退都要听从它。这辆车上还有一个人镇守住它,战事就可以成功。为什么为了伤痛而败坏国君的大事呢?身披盔甲,手执武器,本来就是去走向死亡,伤痛还没到死的地步,您还是尽力而为吧。”一边说,一边用左手把右手的缰绳攥在一起,用空出的右手抓过郤克手中的鼓棰就擂起鼓来。(由于一手控马,)马飞快奔跑而不能停止,晋军队伍跟着指挥车冲上去,把齐军打得打败。晋军随即追赶齐军,三次围绕着华不注山奔跑。

《鞌之战》阅读答案(附翻译)

鞌之战[1]选自《左传·成公二年(即公元前589年)》【原文】癸酉,师陈于鞌[2]。邴夏御齐侯[3],逢丑父为右[4]。晋解张御郤克,郑丘缓为右[5]。齐侯曰:“余姑翦灭此而朝食[6]。”不介马而驰之[7]。郤克伤于矢,流血及屦,未绝鼓音[8],曰:“余病[9]矣!”张侯[10]曰:“自始合,而矢贯余手及肘[11],余折以御,左轮朱殷[12],岂敢言病。吾子[13]忍之!”缓曰:“自始合,苟有险[14],余必下推车,子岂识之[15]?——然子病矣!”张侯曰:“师之耳目,在吾旗鼓,进退从之[16]。此车一人殿之[17],可以集事[18],若之何其以病败君之大事也[19]?擐甲执兵,固即死也[20]。病未及死,吾子勉之[21]!”左并辔[22],右援枹而鼓[23],马逸不能止[24],师从之。齐师败绩[25]。逐之,三周华不注[26]。【注释】 [1]鞌之战:春秋时期的著名战役之一。战争的实质是齐、晋争霸。由于齐侯骄傲轻敌,而晋军同仇敌忾、士气旺盛,战役以齐败晋胜而告终。鞌:通“鞍”,齐国地名,在今山东济南西北。 [2]癸酉:成公二年的六月十七日。师,指齐晋两国军队。陈,列阵,摆开阵势。 [3]邴夏:齐国大夫。御,动词,驾车。御齐侯,给齐侯驾车。齐侯,齐国国君,指齐顷公。 [4]逢丑父:齐国大夫。右:车右。 [5]解张、郑丘缓:都是晋臣,“郑丘”是复姓。郤(xì)克,晋国大夫,是这次战争中晋军的主帅。又称郤献子、郤子等。 [6]姑:副词,姑且。翦灭:消灭,灭掉。朝食:早饭。这里是“吃早饭”的意思。这句话是成语“灭此朝食”的出处。 [7]不介马:不给马披甲。介:甲。这里用作动词,披甲。驰之:驱马追击敌人。之:代词,指晋军。 [8] 未绝鼓音:鼓声不断。古代车战,主帅居中,亲掌旗鼓,指挥军队。“兵以鼓进”,击鼓是进军的号令。 [9] 病:负伤。 [10]张侯,即解张。“张”是字,“侯”是名,人名、字连用,先字后名。 [11]合:交战。贯:穿。肘:胳膊。 [12]朱:大红色。殷:深红色、黑红色。 [13]吾子:您,尊敬。比说“子”更亲切。 [14]苟:连词,表示假设。险:险阻,指难走的路。 [15]识:知道。之,代词,代“苟有险,余必下推车”这件事,可不译。 [16]师之耳目:军队的耳、目(指注意力)。在吾旗鼓:在我们的旗子和鼓声上。进退从之:前进、后退都听从它们。 [17]殿之:镇守它。殿:镇守。 [18]可以集事:可以(之)集事,可以靠它(主帅的车)成事。集事:成事,指战事成功。 [19]若之何:固定格式,一般相当于“对……怎么办”“怎么办”。这里是和语助词“其”配合,放在谓语动词前加强反问,相当于“怎么”“怎么能”。以,介词,因为。败,坏,毁坏。君,国君。大事,感情。古代国家大事有两件:祭祀与战争。这里指战争。 [20]擐:穿上。执兵,拿起武器。 [21]勉,努力。 [22]并,动词,合并。辔(pèi):马缰绳。古代一般是四匹马拉一车,共八条马缰绳,两边的两条系在车上,六条在御者手中,御者双手执之。“左并辔”是说解张把马缰绳全合并到左手里握着。 [23]援:拿过来。枹(fú):击鼓槌。鼓:动词,敲鼓。 [24]逸:奔跑,狂奔。 [25] 败绩:大败。 [26] 周:环绕。华不注:山名,在今山东济南东北。【译文】六月十七日,齐晋两军在鞌地摆开阵势。邴夏为齐侯驾车,逢丑父担任车右做齐侯的护卫。晋军解张替郤克驾车,郑丘缓做了郤克的护卫。齐侯说:“我姑且消灭了晋军再吃早饭!”齐军没有给马披甲就驱车进击晋军。郤克被箭射伤,血一直流到鞋上,但他一直没有停止击鼓进。并说:“我受重伤了!”解张说:“从开始交战,箭就射穿了我的手和胳膊肘,我折断箭杆继续驾车,左边的车轮被血染得深红色,哪里敢说受了重伤?您还是忍住吧。”郑丘缓说:“从开始交战,只要遇到险峻难走的路,我必定要下去推车,您哪里知道这种情况呢?——不过您确实受重伤了!”解张说:“我们的旗帜和战鼓是军队的耳目,或进或退都听从旗鼓指挥。这辆战车只要一人镇守,就可以凭它成事。怎么能因为受伤而败坏国君的大事呢?穿上铠甲,拿起武器,本来就抱定了必死的决心。您虽然受了重伤还没有到死的地步,您就尽最大的努力啊!”于是左手把马缰绳全部握在一起,右手取过鼓槌来击鼓。战马狂奔不止,晋军跟着主帅的车前进。齐军大败,晋军追击齐军,绕着华不注山追了三圈。

齐晋鞌之战的全文翻译

齐晋鞌之战的全文翻译 1、版本一 晋鞌之战(成公二年) 作者:左丘明 【原文】 楚癸酉,师陈于鞌(1)。邴夏御侯,逢丑父为右②。晋解张御克,郑丘缓为右(3)。侯日:“余姑翦灭此而朝食(4)”。不介马而驰之⑤。克伤于矢,流血及屨2未尽∧?6),曰:“余病矣(7)!”张侯曰:“自始合(8),而矢贯余手及肘(9),余折以御,左轮朱殷(10),岂敢言病?吾子忍之!”缓曰:“自始合,苟有险,余必下推车,子岂_识之(11)?然子病矣!”张侯曰:“师之耳目,在吾旗鼓,进退从之。此车一人殿之(12),可以集事(13),若之何其以病败君之大事也?擐甲执兵(14),固即死也(15);病未及死,吾子勉之(16)!”左并辔(17),右援枴?鼓(18)。马逸不能止(19),师从之,师败绩。逐之,三周华不注(20)。 【注释】 ①师:指、晋两国军队。羞:同“鞍”,国地名,在今山东济南西北。②邴(bing)夏:国大夫。侯:顷公。逢丑父:国大夫。右:车右。③解张:晋国大夫,又称张侯。克:即献子,晋国大大,晋军主帅。郑丘缓:晋国大夫,姓郑丘,名缓。(4)姑:暂且。翦灭:消灭。此;指晋军。朝食;吃早饭。⑤不介马:不给马披甲。驰之:驱马追击敌人。(6)未绝鼓音:作战时,主帅亲自掌旗鼓,指挥三军,

所以克受伤后仍然击鼓不停。(7)病:负伤。(8)合:交战。(9)贯:射。穿。肘:胳膊。(10)朱:大红色。殷:深红色。(11)识:知道。 (12) 殿:镇守。(13)集事:成事。(14)擐(huan):穿上。兵:武器。 (15) 即:就。即死:就死,赴死。(16)勉:努力。(17)并:合在一起。辔(Pei):马组绳。(18)援:拉过来。枴〉襲):鼓槌。(19)逸:奔跑,狂奔。(20)周:环绕华不注:山名,在今山东济南东北。 【译文】 六月十七日,国和晋国的军队在鞌摆开了阵势。邴夏为顷公驾车,逢丑父担任车右。晋国解张为卻克驾车,郑丘缓担任车右。顷公说:“我暂且先消灭了这些敌人再吃早饭。”军没有给马披甲就驱车进击晋军。卻克被箭射伤,血流到鞋子上,但他一直没有停止击鼓,并说:“我受伤了!”解张说:“从开始交战,我的手和胳膊就被箭射穿了,我折断了箭,继续驾车,左边的车轮因被血染成了深红色,哪里敢说受了伤?您还是忍住吧?”郑丘缓说:“从开始交战,只要遇到险阻,我一定要下去推车,您哪里知道这些?可是您却受伤了!”解张说:“我们的旗帜和战鼓是军队的耳目,军队进攻和后撤都听从旗鼓指挥。这辆战车只要一个人镇守,就可以成功,怎么能因为负了伤而败坏国君的大事呢?穿上铠甲,拿起武器,本来就是去赴死;受伤不到死的地步,您要奋力而为啊!”解张左手把马绳全部握在一起,右手拿过鼓槌来击鼓。战马狂奔不已,晋军跟著主帅的车前进,军大败,晋军追击军,围著华不注山追了三圈。 2、版本二

齐晋鞌之战原文和译文

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鞍之战翻译

癸酉,师陈于鞌。邴夏御齐侯,逢丑父为右。晋解张御卻克,郑丘缓为右。齐侯曰:“余姑翦灭此而朝食!”不介马而驰之。卻克伤于矢,流血及屦,未绝鼓音。曰:“余病矣!”张侯曰:“自始合,而矢贯余手及肘,余折以御,左轮朱殷。岂敢言病?吾子忍之。”缓曰:“自始合,苟有险,余必下推车。子岂识之?──然子病矣!”张侯曰:“师之耳目,在吾旗鼓,进退从之。此车一人殿之,可以集事。若之何其以病败君之大事也?擐甲执兵,固即死也。病未及死,吾子勉之!”左并辔,右援枹而鼓。马逸不能止,师从之。齐师败绩。逐之,三周华不注。 六月十七日,齐晋两军在鞌地摆开阵势。邴夏为齐侯驾车,逢丑父坐在车右做了齐侯的护卫。晋军解张替卻克驾车,郑丘缓做了卻克的护卫。齐侯说:“我姑且消灭晋军再吃早饭!”不给马披甲就驱车进击晋军。卻克被箭射伤,血一直流到鞋上,但是进军的鼓声仍然没有停息。卻克说:“我受重伤了!”解张说:“从一开始交战,箭就射穿了我的手和胳膊肘,我折断箭杆照样驾车,左边的车轮被血染得殷红,哪里敢说受了重伤?您就忍耐它一点吧。”郑丘缓说:“从开始交战以来,如果遇到险峻难走的路,我必定要下来推车,您是否知道这种情况呢?──不过您的伤势确实太严重了!”解张说:“全军的人都听着我们的鼓声,注视着我们的旗帜,或进或退都跟随着我们。这辆车只要一人镇守,就可以凭它成事。怎么能因受伤而败坏国君的大事呢?穿上铠甲,拿起武器,本来就抱定了必死的决心。受了重伤还没有到死,您还是努力地干吧!”于是左手一并握住缰绳,右手取过鼓

槌击鼓。马狂奔不止,全军跟着他们冲锋。齐军溃败。晋军追击齐军,绕着华不注山追了三圈。 韩厥梦子舆谓己曰:“旦辟左右。”故中御而从齐侯。邴夏曰:“射其御者,君子也。”公曰:“谓之君子而射之,非礼也。”射其左,越于车下;射其右,毙于车中。綦毋张丧车,从韩厥曰:“请寓乘。”从左右,皆肘之,使立于后。韩厥俛定其右。 (头天夜里)韩厥梦见父亲子舆对自己说:“明天早晨不要站住兵车的左右两侧。”因此他就在车当中驾车追赶齐侯。邴夏说:“射那个驾车的,他是个君子。”齐侯说:“认为他是君子反而射他,这不合于礼。”射韩厥的车左,车左坠掉在车下;射他的车右,车右倒在车中。綦毋张的兵车坏了,跟着韩厥说:“请允许我搭你的车。”上车后,綦毋张站在兵车的左边和右边,韩厥都用肘撞他,让他站在身后。韩厥低下身子放稳当被射倒的车右。 逢丑父与公易位。将及华泉,骖絓于木而止。丑父寝于轏中,蛇出于其下,以肱击之,伤而匿之,故不能推车而及。韩厥执絷马前,再拜稽首,奉觞加壁以进,曰:“寡君使群臣为鲁卫请,曰:‘无令舆师陷入君地。’下臣不幸,属当戎行,无所逃隐,且惧奔辟而忝两君。臣辱戎士,敢告不敏,摄官承乏。”丑父使公下,如华泉取饮。郑周父御佐车,宛茷为右,载齐侯以免。韩厥献丑父,邵献子将戮之。呼曰:“自今无有代其君任患者,有一于此,将为戮乎?”卻子曰:“人不难以死免其君,我戮之不祥。赦之,以劝事君者。”乃免之。 逢丑父乘机同齐侯互换了位置。将要到华泉,骖马被树木绊住不能再

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