ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing最新文献_第7页

Frequency hopping patterns for simultaneous multiple-beam sonar imaging 同时多波束声纳成像的跳频模式

ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing Pub Date : 1987-04-06 DOI: 10.1109/ICASSP.1987.1169477

P. Cassereau, J. Jaffe

引用次数: 9

An improved, highly parallel rank-one eigenvector update method with signal processing applications 基于信号处理的一种改进的、高度并行的秩一特征向量更新方法

ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing Pub Date : 1987-04-06 DOI: 10.1109/ICASSP.1987.1169500

R. DeGroat, R. Roberts

引用次数: 4

Context-dependent phonetic Markov models for large vocabulary speech recognition 上下文相关语音马尔可夫模型用于大词汇量语音识别

ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing Pub Date : 1987-04-06 DOI: 10.1109/ICASSP.1987.1169604

Anne-Marie Derouault

{"title":"Context-dependent phonetic Markov models for large vocabulary speech recognition","authors":"Anne-Marie Derouault","doi":"10.1109/ICASSP.1987.1169604","DOIUrl":"https://doi.org/10.1109/ICASSP.1987.1169604","url":null,"abstract":"One approach to large vocabulary speech recognition, is to build phonetic Markov models, and to concatenate them to obtain word models. In previous work, we already designed a recognizer based on 40 phonetic Markov machines, which accepts a 10,000 words vocabulary ([3]), and recently 200,000 words vocabulary ([5]). Since there is one machine per phoneme, these models obviously do not account for coarticulatory effects, which may lead to recognition errors. In this paper, we improve the phonetic models by using general principles about coarticulation effects on automatic phoneme recognition. We show that both the analysis of the errors made by the recognizer, and linguistic facts about phonetic context influence, suggest a method for choosing context dependent models. This method allows to limit the growing of the number of phonems, and still account for the most important coarticulation effects. We present our experiments with a system applying these principles to a set of models for French. With this new system including context-dependant machines, the phoneme recognition rate goes from 82.2% to 85.3%, and the error rate on words with a 10,000 word dictionary, is decreased from 11.2 to 9.8%.","PeriodicalId":140810,"journal":{"name":"ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1987-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115969744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 35

A stochastic segment model for phoneme-based continuous speech recognition 基于音素的连续语音识别随机片段模型

ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing Pub Date : 1987-04-06 DOI: 10.1109/ICASSP.1987.1169700

Salim Roukos, M. O. Dunham

引用次数: 14

Exact recursive least squares algorithms for ARMA modeling 精确递归最小二乘算法的ARMA建模

ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing Pub Date : 1987-04-06 DOI: 10.1109/ICASSP.1987.1169813

S. Prasad, S. Joshi

引用次数: 1

A fast QR/Frequency-domain RLS adaptive filter 一种快速QR/频域RLS自适应滤波器

ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing Pub Date : 1987-04-06 DOI: 10.1109/ICASSP.1987.1169610

J. Cioffi

引用次数: 28

Array signal processing with interconnected Neuron-like elements 用相互连接的神经元样元件处理阵列信号

ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing Pub Date : 1987-04-06 DOI: 10.1109/ICASSP.1987.1169330

R. Rastogi, P. Gupta, R. Kumaresan

引用次数: 45

Distance measure for speech recognition based on the smoothed group delay spectrum 基于平滑群延迟谱的语音识别距离测量

ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing Pub Date : 1987-04-06 DOI: 10.1109/ICASSP.1987.1169476

F. Itakura, T. Umezaki

{"title":"Distance measure for speech recognition based on the smoothed group delay spectrum","authors":"F. Itakura, T. Umezaki","doi":"10.1109/ICASSP.1987.1169476","DOIUrl":"https://doi.org/10.1109/ICASSP.1987.1169476","url":null,"abstract":"We present a novel spectral distance measure based on the smoothed LPC group delay spectrum which gives a stable recognition performance under variable frequency transfer characteristics and additive noise. The weight of the n-th cepstral coefficients in our measure is given byW_{n} = n^{s}. exp(-n^{2}/2tau^{2})which can be adjusted by selecting proper values ofsand τ. In order to optimize the parameters of this distance measure, extensive experiments are carried out in a speaker-dependent isolated word recognition system using a standard dynamic time warping technique. The input speech data used here is a set of phonetically very similar 68 Japanese city name pairs spoken by male speakers. The experimental results show that our distance measure gives a robust recognition rate in spite of the variation in frequency characteristics and signal to noise ratio(SNR). In noisy situations of segmental SNR 20 dB, the recognition rate was more than 13% higher than that obtained by using the standard Euclidean cepstral distance measure. Finally, it is shown that the optimum value ofsis approximately 1, and the optimum range of τΔT is about 1 ms.","PeriodicalId":140810,"journal":{"name":"ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1987-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129386887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 65

Signal representation and processing in the mixed time-frequency domain 混合时频域信号表示与处理

ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing Pub Date : 1987-04-06 DOI: 10.1109/ICASSP.1987.1169487

K. Yu

引用次数: 5

Signal modeling by exponential segments and application in voiced speech analysis 指数分段信号建模及其在语音分析中的应用

ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing Pub Date : 1987-04-06 DOI: 10.1109/ICASSP.1987.1169572

S. Parthasarathy, D. Tufts

{"title":"Signal modeling by exponential segments and application in voiced speech analysis","authors":"S. Parthasarathy, D. Tufts","doi":"10.1109/ICASSP.1987.1169572","DOIUrl":"https://doi.org/10.1109/ICASSP.1987.1169572","url":null,"abstract":"The analysis of signals that can be represented as a linear combination of exponentially damped sinusoids where the values of damping factors, frequencies, and the linear combination coefficients change at certain transition times is considered. These transitions represent the opening and closing of the glottis in the case of speech signals. Techniques are presented for the accurate estimation of the exponential parameters and the times of transition, from noise corrupted observations of the signal. The exponential parameters are obtained by improved linear prediction techniques using low-rank approximations, and further refined by an iterative least-squares technique with stability constraints imposed on the damping factors. Optimal estimates (in the least-squares sense) of the time of transition are presented. Our knowledge of the signal structure is used to obtain improved performance and also a computationally efficient estimation algorithm. Experiments with real, connected speech indicate that the speech waveforms can be accurately represented from a small number of parameters using the analysis presented here.","PeriodicalId":140810,"journal":{"name":"ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1987-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134059338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5