Subword Modeling for Automatic Speech Recognition: Past, Present, and Emerging Approaches

Livescu, Karen; Fosler‐Lussier, Eric; Metze, Florian

doi:10.1109/msp.2012.2210952

Cited by 32 publications

(22 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The idea of using lexicons based on ASWUs instead of the linguistically motivated units has been appealing to the ASR community for three main reasons: (1) ASWUs tend to be rather data-dependent than linguistic knowledgedependent, as they are typically obtained through optimization of an objective function using training speech data (Lee et al, 1988;Bacchiani and Ostendorf, 1998), (2) they could possibly help in handling pronunciation variations (Livescu et al, 2012), and (3) they can avoid the need for explicit phonetic knowledge .…”

Section: Literature Survey On Aswu Derivation and Pronunciation Genermentioning

confidence: 99%

Towards weakly supervised acoustic subword unit discovery and lexicon development using hidden Markov models

Razavi

Rasipuram

Magimai.-Doss

2018

Speech Communication

View full text Add to dashboard Cite

State-of-the-art automatic speech recognition and text-to-speech systems are based on subword units, typically phonemes. This necessitates a lexicon that maps each word to a sequence of subword units. Development of a phonetic lexicon for a language requires linguistic knowledge as well as human effort, which may not be always readily available, particularly for under-resourced languages.In such scenarios, an alternative approach is to use a lexicon based on units such as, graphemes or subword units automatically derived from the acoustic data. This article focuses on automatic subword unit based lexicon development using methods that are employed for development of grapheme-based systems.Specifically, we present a novel hidden Markov model (HMM) based 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 by learning either a deterministic or a probabilistic 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 under-resourced language, * Corresponding author Email addresses: marzieh.razavi@idiap.ch (Marzieh Razavi), ramya.murali@gmail.com (Ramya Rasipuram), mathew@idiap.ch (Mathew Magimai.-Doss) Preprint submitted to ElsevierMarch 17, 2017and comparing the approach against state-of-the-art grapheme-based ASR approaches. 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 transferred across domains. The experimental studies on Scottish Gaelic show that the proposed ASWU-based lexicon development approach scales without any language specific considerations and leads to better ASR systems compared to a grapheme-based lexicon, including the case where ASR system performance is boosted through the use of acoustic models built with multilingual resources from resource-rich languages.

show abstract

Section: Literature Survey On Aswu Derivation and Pronunciation Genermentioning

confidence: 99%

Towards weakly supervised acoustic subword unit discovery and lexicon development using hidden Markov models

Razavi

Rasipuram

Magimai.-Doss

2018

Speech Communication

View full text Add to dashboard Cite

show abstract

“…Constructing speech processing systems for a language usually implies developing an ASR. This requires a significant amount of time-coded and transcribed speech data along with the corresponding text and pronunciation dictionary (Livescu et al, 2012;Rabiner et al, 1993). Unless the training corpus is read speech based on a script, researchers have usually had to provide transcriptions by hand.…”

Section: A Forced Alignmentmentioning

confidence: 99%

“…This is especially useful for underresourced languages lacking a substantial set of training data for a complete ASR system (Badenhorst et al, 2011;Livescu et al, 2012;Li, 2008a, 2008b). The accuracy of this strategy depends closely on the fit between the phone set used to build the aligner and the phone set of the target language.…”

Section: A Forced Alignmentmentioning

confidence: 99%

Using automatic alignment to analyze endangered language data: Testing the viability of untrained alignment

DiCanio

Nam

Whalen

et al. 2013

The Journal of the Acoustical Society of America

View full text Add to dashboard Cite

While efforts to document endangered languages have steadily increased, the phonetic analysis of endangered language data remains a challenge. The transcription of large documentation corpora is, by itself, a tremendous feat. Yet, the process of segmentation remains a bottleneck for research with data of this kind. This paper examines whether a speech processing tool, forced alignment, can facilitate the segmentation task for small data sets, even when the target language differs from the training language. The authors also examined whether a phone set with contextualization outperforms a more general one. The accuracy of two forced aligners trained on English (HMALIGN and P2FA) was assessed using corpus data from Yolox ochitl Mixtec. Overall, agreement performance was relatively good, with accuracy at 70.9% within 30 ms for HMALIGN and 65.7% within 30 ms for P2FA. Segmental and tonal categories influenced accuracy as well. For instance, additional stop allophones in HMALIGN's phone set aided alignment accuracy. Agreement differences between aligners also corresponded closely with the types of data on which the aligners were trained. Overall, using existing alignment systems was found to have potential for making phonetic analysis of small corpora more efficient, with more allophonic phone sets providing better agreement than general ones.

show abstract

“…However, the subword units can be derived automatically from the speech signal which can possibly help in better handling of pronunciation variations [1]. Moreover, with growing interest in development of ASR systems for under-resourced languages, attempts have been made to automatically derive subword units as well as the pronunciations based on such units.…”

Section: Introductionmentioning

confidence: 99%

An HMM-based formalism for automatic subword unit derivation and pronunciation generation

Razavi

Magimai.-Doss

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

We propose 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 maximum-likelihood criterion. The subword unit based pronunciations are then learned in the framework of Kullback-Leibler divergence based HMM. The automatic speech recognition (ASR) experiments on WSJ0 English corpus show that the approach leads to 12.7% relative reduction in word error rate compared to grapheme-based system. Our approach can be beneficial in reducing the need for expert knowledge in development of ASR as well as text-to-speech systems.

show abstract

Subword Modeling for Automatic Speech Recognition: Past, Present, and Emerging Approaches

Cited by 32 publications

References 54 publications

Towards weakly supervised acoustic subword unit discovery and lexicon development using hidden Markov models

Towards weakly supervised acoustic subword unit discovery and lexicon development using hidden Markov models

Using automatic alignment to analyze endangered language data: Testing the viability of untrained alignment

An HMM-based formalism for automatic subword unit derivation and pronunciation generation

Contact Info

Product

Resources

About