Non-linguistic features such as vocal tract shapes and acoustic devices are inevitably involved in speech. Recently, a new representation of speech without any dimensions indicating the non-linguistic features was proposed. It discards the absolute properties of speech events and captures only the interrelations among them. In this paper, recognition experiments of continuous utterances of Japa...

Feature extraction and selection for continuous speech recognition is a complex task. State of the art speech recognition systems use features that are derived by ignoring the Fourier transform phase. In our earlier studies we have shown the efficacy of The Modified Group Delay Feature (MODGDF) derived from the Fourier transform phase for phoneme, syllable and speaker recognition. In this paper...

This paper addresses the issue of learning hidden Markov model (HMM) parameters for speaker-independent continuous speech recognition. Bahl et al. [Bahl 88a] introduced the corrective training algorithm for speaker-dependent isolated word recognition. Their algorithm attempted to improve the recognition accuracy on the training data. In this work, we extend this algorithm to speaker-independent...

Speech-to-text conversion of French necessitates that both the acoustic level recognition and language modeling be tailored to the French language. Work in this area was initiated at LIMSI over 10 years ago. In this paper a summary of the ongoing research in this direction is presented. Included are studies on distributional properties of French text materials; problems speciic to speech-to-tex...

This paper describes some of the most recent work on continuous speech recognition using phonemelevel hidden Markov models (HMMs) which has been conducted at the UK Speech Research Unit as part of the ARM (Airborne Reconnaissance Mission) project [11]. The goal of the project is automatic recognition of spoken airborne reconnaissance reports. The project draws on many years of research undertak...

The Multi Stream automatic speech recognition approach was investigated in this work as a framework for Au dio Visual data fusion and speech recognition This method presents many potential advantages for such a task It particularly allows for synchronous decoding of continuous speech while still allowing for some asynchrony of the visual and acoustic information streams First the Multi Stream f...

Transcribing dysarthric speech into text is still a challenging problem for the state-of-the-art techniques or commercially available speech recognition systems. Improving the accuracy of dysarthric speech recognition, this paper adopts Deep Belief Neural Networks (DBNs) to model the distribution of dysarthric speech signal. A continuous dysarthric speech recognition system is produced, in whic...

Today’s state-of-the-art speech recognition systems typically use continuous density hidden Markov models with mixture of Gaussian distributions. Such speech recognition systems have problems; they require too much memory to run, and are too slow for large vocabulary applications. Two approaches are proposed for the design of compact acoustic models, namely, subspace distribution clustering hid...

In this paper, the problem of robust speech recognition has been considered. Our approach is based on the noise reduction of the parameters that we use for recognition, that is, the Mel-based cepstral coefficients. A Temporal-Correlation-Based Recurrent Multilayer Neural Network (TCRMNN) for noise reduction in the cepstral domain is used in order to get less-variant parameters to be useful for ...

This paper presents an initial study to perform Iarge-vocabuIary continuous Mandarin speech recognition based on a Segmental Probability Model(SPM) approach. SPM was first proposed for recognition of isolated Mandarin syllables, in which every syllable must be equally segmented before recognition. Therefore, A concatenated syllable matching algorithm in place of the conventional Viterbi search ...