• Title, Summary, Keyword: Speech recognition

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Performance Comparison of the Speech Enhancement Methods for Noisy Speech Recognition (잡음음성인식을 위한 음성개선 방식들의 성능 비교)

  • Chung, Yong-Joo
    • Phonetics and Speech Sciences
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    • v.1 no.2
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    • pp.9-14
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    • 2009
  • Speech enhancement methods can be generally classified into a few categories and they have been usually compared with each other in terms of speech quality. For the successful use of speech enhancement methods in speech recognition systems, performance comparisons in terms of speech recognition accuracy are necessary. In this paper, we compared the speech recognition performance of some of the representative speech enhancement algorithms which are popularly cited in the literature and used widely. We also compared the performance of speech enhancement methods with other noise robust speech recognition methods like PMC to verify the usefulness of speech enhancement approaches in noise robust speech recognition systems.

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Google speech recognition of an English paragraph produced by college students in clear or casual speech styles (대학생들이 또렷한 음성과 대화체로 발화한 영어문단의 구글음성인식)

  • Yang, Byunggon
    • Phonetics and Speech Sciences
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    • v.9 no.4
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    • pp.43-50
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    • 2017
  • These days voice models of speech recognition software are sophisticated enough to process the natural speech of people without any previous training. However, not much research has reported on the use of speech recognition tools in the field of pronunciation education. This paper examined Google speech recognition of a short English paragraph produced by Korean college students in clear and casual speech styles in order to diagnose and resolve students' pronunciation problems. Thirty three Korean college students participated in the recording of the English paragraph. The Google soundwriter was employed to collect data on the word recognition rates of the paragraph. Results showed that the total word recognition rate was 73% with a standard deviation of 11.5%. The word recognition rate of clear speech was around 77.3% while that of casual speech amounted to 68.7%. The reasons for the low recognition rate of casual speech were attributed to both individual pronunciation errors and the software itself as shown in its fricative recognition. Various distributions of unrecognized words were observed depending on each participant and proficiency groups. From the results, the author concludes that the speech recognition software is useful to diagnose each individual or group's pronunciation problems. Further studies on progressive improvements of learners' erroneous pronunciations would be desirable.

Recognition of Emotion and Emotional Speech Based on Prosodic Processing

  • Kim, Sung-Ill
    • The Journal of the Acoustical Society of Korea
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    • v.23 no.3E
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    • pp.85-90
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    • 2004
  • This paper presents two kinds of new approaches, one of which is concerned with recognition of emotional speech such as anger, happiness, normal, sadness, or surprise. The other is concerned with emotion recognition in speech. For the proposed speech recognition system handling human speech with emotional states, total nine kinds of prosodic features were first extracted and then given to prosodic identifier. In evaluation, the recognition results on emotional speech showed that the rates using proposed method increased more greatly than the existing speech recognizer. For recognition of emotion, on the other hands, four kinds of prosodic parameters such as pitch, energy, and their derivatives were proposed, that were then trained by discrete duration continuous hidden Markov models(DDCHMM) for recognition. In this approach, the emotional models were adapted by specific speaker's speech, using maximum a posteriori(MAP) estimation. In evaluation, the recognition results on emotional states showed that the rates on the vocal emotions gradually increased with an increase of adaptation sample number.

Digit Recognition using Speech and Image Information (음성과 영상 정보를 이용한 우리말 숫자음 인식)

  • 이종혁;최재원
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.6 no.1
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    • pp.83-88
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    • 2002
  • In the majority of case, speech recognition method tried recognition using only speech information In order to highten the recognition rate, we proposed recognition system that recognige digit using speech and image information. Through an experiment, this paper compared the recognition rate performed by existent speech recognition method and speech recognition method that includes image information. When we added the image information to the speech information, the speech recognition rate was increased about 6%. This paper shows that adding image information to speech information is more effective than using only speech information In digit recognition.

Robust Distributed Speech Recognition under noise environment using MESS and EH-VAD (멀티밴드 스펙트럼 차감법과 엔트로피 하모닉을 이용한 잡음환경에 강인한 분산음성인식)

  • Choi, Gab-Keun;Kim, Soon-Hyob
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.48 no.1
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    • pp.101-107
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    • 2011
  • The background noises and distortions by channel are major factors that disturb the practical use of speech recognition. Usually, noise reduce the performance of speech recognition system DSR(Distributed Speech Recognition) based speech recognition also bas difficulty of improving performance for this reason. Therefore, to improve DSR-based speech recognition under noisy environment, this paper proposes a method which detects accurate speech region to extract accurate features. The proposed method distinguish speech and noise by using entropy and detection of spectral energy of speech. The speech detection by the spectral energy of speech shows good performance under relatively high SNR(SNR 15dB). But when the noise environment varies, the threshold between speech and noise also varies, and speech detection performance reduces under low SNR(SNR 0dB) environment. The proposed method uses the spectral entropy and harmonics of speech for better speech detection. Also, the performance of AFE is increased by precise speech detections. According to the result of experiment, the proposed method shows better recognition performance under noise environment.

A MFCC-based CELP Speech Coder for Server-based Speech Recognition in Network Environments (네트워크 환경에서 서버용 음성 인식을 위한 MFCC 기반 음성 부호화기 설계)

  • Lee, Gil-Ho;Yoon, Jae-Sam;Oh, Yoo-Rhee;Kim, Hong-Kook
    • MALSORI
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    • no.54
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    • pp.27-43
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    • 2005
  • Existing standard speech coders can provide speech communication of high quality while they degrade the performance of speech recognition systems that use the reconstructed speech by the coders. The main cause of the degradation is that the spectral envelope parameters in speech coding are optimized to speech quality rather than to the performance of speech recognition. For example, mel-frequency cepstral coefficient (MFCC) is generally known to provide better speech recognition performance than linear prediction coefficient (LPC) that is a typical parameter set in speech coding. In this paper, we propose a speech coder using MFCC instead of LPC to improve the performance of a server-based speech recognition system in network environments. However, the main drawback of using MFCC is to develop the efficient MFCC quantization with a low-bit rate. First, we explore the interframe correlation of MFCCs, which results in the predictive quantization of MFCC. Second, a safety-net scheme is proposed to make the MFCC-based speech coder robust to channel error. As a result, we propose a 8.7 kbps MFCC-based CELP coder. It is shown from a PESQ test that the proposed speech coder has a comparable speech quality to 8 kbps G.729 while it is shown that the performance of speech recognition using the proposed speech coder is better than that using G.729.

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Emotion Recognition using Robust Speech Recognition System (강인한 음성 인식 시스템을 사용한 감정 인식)

  • Kim, Weon-Goo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.18 no.5
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    • pp.586-591
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    • 2008
  • This paper studied the emotion recognition system combined with robust speech recognition system in order to improve the performance of emotion recognition system. For this purpose, the effect of emotional variation on the speech recognition system and robust feature parameters of speech recognition system were studied using speech database containing various emotions. Final emotion recognition is processed using the input utterance and its emotional model according to the result of speech recognition. In the experiment, robust speech recognition system is HMM based speaker independent word recognizer using RASTA mel-cepstral coefficient and its derivatives and cepstral mean subtraction(CMS) as a signal bias removal. Experimental results showed that emotion recognizer combined with speech recognition system showed better performance than emotion recognizer alone.

Real-Time Implementation of Wireless Remote Control of Mobile Robot Based-on Speech Recognition Command (음성명령에 의한 모바일로봇의 실시간 무선원격 제어 실현)

  • Shim, Byoung-Kyun;Han, Sung-Hyun
    • Journal of the Korean Society of Manufacturing Technology Engineers
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    • v.20 no.2
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    • pp.207-213
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    • 2011
  • In this paper, we present a study on the real-time implementation of mobile robot to which the interactive voice recognition technique is applied. The speech command utters the sentential connected word and asserted through the wireless remote control system. We implement an automatic distance speech command recognition system for voice-enabled services interactively. We construct a baseline automatic speech command recognition system, where acoustic models are trained from speech utterances spoken by a microphone. In order to improve the performance of the baseline automatic speech recognition system, the acoustic models are adapted to adjust the spectral characteristics of speech according to different microphones and the environmental mismatches between cross talking and distance speech. We illustrate the performance of the developed speech recognition system by experiments. As a result, it is illustrated that the average rates of proposed speech recognition system shows about 95% above.

Pattern Recognition Methods for Emotion Recognition with speech signal

  • Park Chang-Hyun;Sim Kwee-Bo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.6 no.2
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    • pp.150-154
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    • 2006
  • In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition are determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section.

Comparison of HMM models and various cepstral coefficients for Korean whispered speech recognition (은닉 마코프 모델과 켑스트럴 계수들에 따른 한국어 속삭임의 인식 비교)

  • Park, Chan-Eung
    • 전자공학회논문지 IE
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    • v.43 no.2
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    • pp.22-29
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    • 2006
  • Recently the use of whispered speech has increased due to mobile phone and the necessity of whispered speech recognition is increasing. So various feature vectors, which are mainly used for speech recognition, are applied to their HMMs, normal speech models, whispered speech models, and integrated models with normal speech and whispered speech so as to find out suitable recognition system for whispered speech. The experimental results of recognition test show that the recognition rate of whispered speech applied to normal speech models is too low to be used in practical applications, but separate whispered speech models recognize whispered speech with the highest rates at least 85%. And also integrated models with normal speech and whispered speech score acceptable recognition rate but more study is needed to increase recognition rate. MFCE and PLCC feature vectors score higher recognition rate when applied to separate whispered speech models, but PLCC is the best when a lied to integrated models with normal speech and whispered speech.