Developing an HMM-Based Speech Synthesis System for Malay: A Comparison of Iterative and Isolated Unit Training

Mustafa, Mumtaz Begum; Don, Zuraidah Mohd; Ainon, Raja Noor; Zainuddin, Roziati; Knowles, Gerry

doi:10.1587/transinf.e97.d.1273

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…The availability of databases is not challenging in bilingual and multilingual ASR systems for well-resourced languages [28,44,45]. Bilingual and multilingual speech recognition faces challenges in the availability of databases for under-resourced languages, with many having to first develop their databases [5,34,36,39,64,65]. However, the continuing interest in CS has resulted in the development of standard databases in recent research on CS [2,4,6,30,40,47,[51][52][53][54][55].…”

Section: Databasesmentioning

confidence: 99%

Code-Switching in Automatic Speech Recognition: The Issues and Future Directions

et al. 2022

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Code-switching (CS) in spoken language is where the speech has two or more languages within an utterance. It is an unsolved issue in automatic speech recognition (ASR) research as ASR needs to recognise speech in bilingual and multilingual settings, where the accuracy of ASR systems declines with CS due to pronunciation variation. There are very few reviews carried out on CS, with none conducted on bilingual and multilingual CS ASR systems. This study investigates the importance of CS in bilingual and multilingual speech recognition systems. To meet the objective of this study, two research questions were formulated, which cover both the current issues and the direction of the research. Our review focuses on databases, acoustic and language modelling, and evaluation metrics. Using selected keywords, this research has identified 274 papers and selected 42 experimental papers for review, of which 24 (representing 57%) have discussed CS, while the rest look at multilingual ASR research. The selected papers cover many well-resourced and under-resourced languages, and novel techniques to manage CS in ASR systems, which are mapping, combining and merging the phone sets of the languages experimented with in the research. Our review also examines the performance of those methods. This review found a significant variation in the performance of CS in terms of word error rates, indicating an inconsistency in the ability of ASRs to handle CS. In the conclusion, we suggest several future directions that address the issues identified in this review.

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Section: Databasesmentioning

confidence: 99%

Code-Switching in Automatic Speech Recognition: The Issues and Future Directions

et al. 2022

Self Cite

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“…These resulting values (n = 880) were used for analysis. [6], [7], [8], [9], [10], [11], [12] Hidden Markov Model synthesis (HMM) 7 [12], [13], [14], [15], [16], [17], [18] Neural network (non-S2S) synthesis (DNN) 9 [19], [20], [21], [22], [23], [24], [25], [26], [27] Sequence-to-sequence synthesis (S2S)…”

Section: Characteristics Of the Included Studiesmentioning

confidence: 99%

“…Intelligibility [23] WER (Word Error Rate) Intelligibility [20] MOS (Mean Opinion Score) Naturalness/ Quality [6], [8], [9], [14], [15], [16], [17], [18], [21], [23] A/B Preference (preference rate b/w test & control) Quality [5], [10], [11], [12], [13]…”

Section: Multilingual Model Effect (Mlme)mentioning

confidence: 99%

A Systematic Review and Analysis of Multilingual Data Strategies in Text-to-Speech for Low-Resource Languages

Do¹,

Coler²,

Dijkstra³

et al. 2021

Interspeech 2021

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We provide a systematic review of past studies that use multilingual data for text-to-speech (TTS) of low-resource languages (LRLs). We focus on the strategies used by these studies for incorporating multilingual data and how they affect output speech quality. To investigate the difference in output quality between corresponding monolingual and multilingual models, we propose a novel measure to compare this difference across the included studies and their various evaluation metrics. This measure, called the Multilingual Model Effect (MLME), is found to be affected by: acoustic model architecture, the difference ratio of target language data between corresponding multilingual and monolingual experiments, the balance ratio of target language data to total data, and the amount of target language data used. These findings can act as reference for data strategies in future experiments with multilingual TTS models for LRLs. Language family classification, despite being widely used, is not found to be an effective criterion for selecting source languages.

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Prosody Analysis of Malay Language Storytelling Corpus

Ramli

Seman

Ardi

et al. 2016

Speech and Computer

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Developing an HMM-Based Speech Synthesis System for Malay: A Comparison of Iterative and Isolated Unit Training

Cited by 4 publications

References 24 publications

Code-Switching in Automatic Speech Recognition: The Issues and Future Directions

Code-Switching in Automatic Speech Recognition: The Issues and Future Directions

A Systematic Review and Analysis of Multilingual Data Strategies in Text-to-Speech for Low-Resource Languages

Prosody Analysis of Malay Language Storytelling Corpus

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