Acoustic Analysis of Syllables Across Indian Languages

Prakash, Anusha; Prakash, Jeena J.; Murthy, Hema A.

doi:10.21437/interspeech.2016-1127

Cited by 8 publications

(8 citation statements)

References 13 publications

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“…Phonotactic differences are more evident across language groups (Indo-Aryan and Dravidian). For example, Dravidian languages are characterised by their agglutinative nature [19]. Language-specific phones also affect the phonotactics of languages.…”

Section: Motivationmentioning

confidence: 99%

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Code-switching in Indic Speech Synthesisers

et al. 2018

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Most Indians are inherently bilingual or multilingual owing to the diverse linguistic culture in India. As a result, code-switching is quite common in conversational speech. The objective of this work is to train good quality text-to-speech (TTS) synthesisers that can seamlessly handle code-switching. To achieve this, bilingual TTSes that are capable of handling phonotactic variations across languages are trained using combinations of monolingual data in a unified framework. In addition to segmenting Indic speech data using signal processing cues in tandem with hidden Markov model-deep neural network (HMM-DNN), we propose to segment Indian English data using the same approach after NIST syllabification. Then, bilingual HTS-STRAIGHT based systems are trained by randomizing the order of data so that the systematic interactions between the two languages are captured better. Experiments are conducted by considering three language pairs: Hindi+English, Tamil+English and Hindi+Tamil. The code-switched systems are evaluated on monolingual, code-mixed and code-switched texts. Degradation mean opinion score (DMOS) for monolingual sentences shows marginal degradation over that of an equivalent monolingual TTS system, while the DMOS for bilingual sentences is significantly better than that of the corresponding monolingual TTS systems.

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Section: Motivationmentioning

confidence: 99%

“…Language-specific phones also affect the phonotactics of languages. Complex phone clusters (for example-strength, twelfth) that occur in English are rare in Indian languages [19]. Indian languages are akshara-based and have simpler phone clusters [19].…”

Section: Motivationmentioning

confidence: 99%

Code-switching in Indic Speech Synthesisers

et al. 2018

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“…The common phonetic characteristics of different languages enable the use of pooled resources across languages, where pooling is performed based on the phonetic content. Motivated by the work in [5,6,7], where similarities among Indian languages are studied and exploited, the focus is on capitalising on these similarities for system building. In this context, two types of text representations are explored in the end-to-end framework-character-based and phone-based.…”

Section: Introductionmentioning

confidence: 99%

Building Multilingual End-to-End Speech Synthesisers for Indian Languages

Prakash¹,

Thomas²,

Umesh³

et al. 2019

10th ISCA Workshop on Speech Synthesis (SSW 10)

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Building text-to-speech (TTS) synthesisers is a difficult task, especially for low resource languages. Language-specific modules need to be developed for system building. End-to-end speech synthesis has become a popular paradigm as a TTS can be trained using only pairs. However, end-to-end speech synthesis is not scalable in a multilanguage scenario, as the vocabulary increases with the number of different scripts. In this paper, TTSes are trained for Indian languages using two text representations-character-based and phone-based. For the character-based approach, a multi-language character map (MLCM) is proposed to easily train Indic speech synthesisers. The phone-based approach uses the common label set (CLS) representation for Indian languages. Both approaches leverage the similarities that exist among the languages. The advantage is a compact representation across multiple languages. Experiments are conducted by building TTSes using monolingual data and by pooling data across two languages. The ability to synthesise code-mixed text using the phone-based approach is also assessed. Subjective evaluations indicate that reasonably good quality Indic TTSes can be developed using both approaches. This emphasises the need to incorporate multilingual text processing in the end-to-end framework.

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“…This is due to the fact that Indian languages are phrase-based in terms of production and perception (Féry, 2010; J.J. Prakash & Murthy, 2019). Hence, much of the punctuation prediction research for these languages are mapped to phrase-break prediction task (i.e., predicting the presence or absence of a break) predominantly for improving the naturalness of synthetic speech in speech synthesis systems (Vadapalli et al, 2013;Sarkar & Rao, 2015;A. Prakash et al, 2016; J.J. Prakash & Murthy, 2019).…”

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confidence: 99%

“…Since Indian languages are syllable-timed (A. Prakash et al, 2016), much of the previous research involving phrase-break prediction (predominantly for speech synthesis systems) revolve around syllabic features.…”

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Feature‐weightedAdaBoostclassifier for punctuation prediction in Tamil and HindiNLPsystems

2021

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Punctuation marks play a vital role in text representation and interpretation, and are useful in enhancing the performance of modern Natural Language Processing (NLP) systems such as voice input typing aids, machine translation, and speech synthesis systems. Punctuation marks, except period, are inherently not available in Indian languages such as Tamil and Hindi. However, some modern forms of writing such as news articles, blogs, stories, and so forth, incorporate user‐defined punctuation marks in these languages. The current work proposes an automatic punctuation prediction system for texts in Tamil and Hindi using classification approach, where punctuation prediction is considered as a multi‐class classification problem. Word‐level text features are chosen and are analysed to validate their language‐dependency and significance towards punctuation prediction. A Feature‐weighted AdaBoost (FAda) classifier is proposed that defines a novel boosting factor to adjust the hypothesis weight of the weak classifiers, hence reducing the number of false classifications. It is observed that the proposed classifier outperforms the other classification techniques such as, AdaBoost, SVM, CART, CRF, and Bi‐LSTM by a maximum difference of 50% and 16% in the macro F1‐scores for Tamil and Hindi texts, respectively. The proposed classifier performs on par with the attention‐based classifier for both Tamil and Hindi texts. Further, as a proof of concept, the proposed punctuation prediction system is applied to voice keyboard, machine translation, and speech synthesis systems, to validate the effect of the punctuation marks on the performance of these Natural Language Processing (NLP) systems.

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Acoustic Analysis of Syllables Across Indian Languages

Cited by 8 publications

References 13 publications

Code-switching in Indic Speech Synthesisers

Code-switching in Indic Speech Synthesisers

Building Multilingual End-to-End Speech Synthesisers for Indian Languages

Feature‐weightedAdaBoostclassifier for punctuation prediction in Tamil and HindiNLPsystems

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