Olivier Deroo

Automatic speech recognition and speech variability: A review

Major progress is being recorded regularly on both the technology and exploitation of automatic speech recognition (ASR) and spoken language systems. However, there are still technological barriers to flexible solutions and user satisfaction under some circumstances. This is related to several factors, such as the sensitivity to the environment (background noise), or the weak representation of grammatical and semantic knowledge. Current research is also emphasizing deficiencies in dealing with variation naturally present in speech. For instance, the lack of robustness to foreign accents precludes the use by specific populations. Also, some applications, like directory assistance, particularly stress the core recognition technology due to the very high active vocabulary (application perplexity). There are actually many factors affecting the speech realization: regional, sociolinguistic, or related to the environment or the speaker herself. These create a wide range of variations that may not be modeled correctly (speaker, gender, speaking rate, vocal effort, regional accent, speaking style, non-stationarity, etc.), especially when resources for system training are scarce. This paper outlines current advances related to these topics.

Phonetic alignment: speech synthesis-based vs. viterbi-based

In this paper we compare two different methods for automatically phonetically labeling a continuous speech data-base, as usually required for designing a speech recognition or speech synthesis system. The first method is based on temporal alignment of speech on a synthetic speech pattern; the second method uses either a continuous density hidden Markov models (HMM) or a hybrid HMM/ANN (artificial neural network) system in forced alignment mode. Both systems have been evaluated on read utterances not part of the training set of the HMM systems, and compared to manual segmentation. This study outlines the advantages and drawbacks of both methods. The speech synthetic system has the great advantage that no training stage (hence no large labeled database) is needed, while HMM Systems easily handle multiple phonetic transcriptions (phonetic lattice). We deduce a method for the automatic creation of large phonetically labeled speech databases, based on using the synthetic speech segmentation tool to bootstrap the training process of either a HMM or a hybrid HMM/ANN system. The importance of such segmentation tools is a key point for the development of improved multilingual speech synthesis and recognition systems.

Hybrid HMM/ANN systems for training independent tasks: experiments on Phonebook and related improvements

In this paper, we evaluate multi-Gaussian HMM systems and hybrid HMM/ANN systems in the framework of task independent training for small size (75 words) and medium size (600 words) vocabularies. To do this, we use the Phonebook database (Pitrelli et al., 1995) which is particularly well suited to this kind of experiment since (1) it is a very large telephone database and (2) the size and content of the test vocabulary is very flexible. For each system, different HMM topologies are compared to test the influence of state tying (with a number of parameters approximately kept constant) on the recognition performance. Two lexica (Phonebook and CMU) are also compared and it is shown that the CMU lexicon leads to significantly better performance. Finally, it is shown that with a quite simple system and a few adaptations to the basic HMM/ANN scheme, recognition performance of 98.5% and 94.7% can easily be achieved, respectively on a lexicon of 75 and 600 words (isolated words, telephone speech and lexicon words not present in the training data).

Feature extraction and acoustic modeling: an approach for improved generalization across languages and accents

The paper proposes a solution that brings some advances to the genericity of the ASR technology towards tasks and languages. A non-linear discriminant model is built from multi-lingual, multi-task speech material in order to classify the acoustic signal into language independent phonetic units. Instead of considering this model for direct HMM state likelihood estimation, it rather operates as a first stage to produce discriminant features that can be further used in cascade with a traditional task/language specific ASR system. This first stage structure is expected to achieve a strong modeling of the cross-language variability of speech that can better handle pronunciation variations due for instance to regional and non-native accents. Moreover, the flexibility of this architecture still allow the development of small task/language dedicated ASR systems as a second stage structure, possibly with small amount of data. The benefit of this architecture is demonstrated through a fine analysis of modeling performance at the phoneme level and on two different isolated word recognition tasks featuring accent variabilities

Automatic Speech Recognition and Intrinsic Speech Variation

This paper briefly reviews state of the art related to the topic of speech variability sources in automatic speech recognition systems. It focuses on some variations within the speech signal that make the ASR task difficult. The variations detailed in the paper are intrinsic to the speech and affect the different levels of the ASR processing chain. For different sources of speech variation, the paper summarizes the current knowledge and highlights specific feature extraction or modeling weaknesses and current trends

Editorial: Introduction to the Special Issue on Intrinsic Speech Variations

correspondence: Corresponding author. Tel.: +04 93 00 26 28; fax: +04 93 00 26 27. (Wellekens, Christian)