Weighted finite-state transducers in speech recognition : a compaction algorithm for non-determinizable transducers

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal

Towards Weakly Supervised Acoustic Subword Unit Discovery and Lexicon Development Using Hidden Markov Models

Developing a phonetic lexicon for a language requires linguistic knowledge as well as human effort, which may not be available, particularly for under-resourced languages. An alternative to development of a phonetic lexicon is to automatically derive subword units using acoustic information and generate associated pronunciations. In the literature, this has been mostly studied from the pronunciation variation modeling perspective. In this article, we investigate automatic subword unit derivation from the under-resourced language point of view. Towards that, we present a novel hidden Markov model (HMM) formalism for automatic derivation of subword units and pronunciation generation using only transcribed speech data. In this approach, the subword units are derived from the clustered context-dependent units in a grapheme based system using the maximum-likelihood criterion. The subword unit based pronunciations are then generated either by deterministic or probabilistic learning of the relationship between the graphemes and the acoustic subword units (ASWUs). In this article, we first establish the proposed framework on a well resourced language by comparing it against related approaches in the literature and investigating the transferability of the derived subword units to other domains. We then show the scalability of the proposed approach on real under-resourced scenarios by conducting studies on Scottish Gaelic, a genuinely minority and endangered language, and comparing the approach against state-of-the-art grapheme-based approaches in under-resourced scenarios. Our experimental studies on English show that the derived subword units can not only lead to better ASR systems compared to graphemes, but can also be exploited to build out-of-domain ASR systems. The experimental studies on Scottish Gaelic show that the proposed ASWU-based lexicon development approach retains its dominance over grapheme-based lexicon. Alternately, the proposed approach yields significant gains in ASR performance, even when multilingual resources from resource-rich languages are exploited in the development of ASR systems

Context-dependent modeling in a segment-based speech recognition system

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (leaves 78-80).by Benjamin M. Serridge.M.Eng

Context-Sensitive Hidden Markov Models for Modeling Long-Range Dependencies in Symbol Sequences

The hidden Markov model (HMM) has been widely used in signal processing and digital communication applications. It is well known for its efficiency in modeling short-term dependencies between adjacent symbols. However, it cannot be used for modeling long-range interactions between symbols that are distant from each other. In this paper, we introduce the concept of context-sensitive HMM. The proposed model is capable of modeling strong pairwise correlations between distant symbols. Based on this model, we propose dynamic programming algorithms that can be used for finding the optimal state sequence and for computing the probability of an observed symbol string. Furthermore, we also introduce a parameter re-estimation algorithm, which can be used for optimizing the model parameters based on the given training sequences

Subword lexical modelling for speech recognition

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references (p. 155-160).by Raymond Lau.Ph.D