Significance of Pseudo-syllables in building better acoustic models for Indian English TTS

Vignesh, S.; Shanmugam, S. Aswin; Murthy, Hema A.

doi:10.1109/icassp.2016.7472753

Cited by 7 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, spectral flux is used to address the issues of inaccurate syllable boundaries in the context of fricatives, affricates, nasals and semivowels. A modified version of spectral flux called sub-band spectral flux (SBSF) is used as a cue for boundary correction [14,15]. The correction of the boundary between two syllables, syllable 1 and syllable 2, is hence performed on the basis of end phone of syllable 1 and start phone of syllable 2.…”

Section: Acoustic Cues For Boundary Correctionmentioning

confidence: 99%

Deep Learning Techniques in Tandem with Signal Processing Cues for Phonetic Segmentation for Text to Speech Synthesis in Indian Languages

et al. 2017

Self Cite

View full text Add to dashboard Cite

Automatic detection of phoneme boundaries is an important sub-task in building speech processing applications, especially text-to-speech synthesis (TTS) systems. The main drawback of the Gaussian mixture model-hidden Markov model (GMM-HMM) based forced-alignment is that the phoneme boundaries are not explicitly modeled. In an earlier work, we had proposed the use of signal processing cues in tandem with GMM-HMM based forced alignment for boundary correction for building Indian language TTS systems. In this paper, we capitalise on the ability of robust acoustic modeling techniques such as deep neural networks (DNN) and convolutional deep neural networks (CNN) for acoustic modeling. The GMM-HMM based forced alignment is replaced by DNN-HMM/CNN-HMM based forced alignment. Signal processing cues are used to correct the segment boundaries obtained using DNN-HMM/CNN-HMM segmentation. TTS systems built using these boundaries show a relative improvement in synthesis quality.

show abstract

Section: Acoustic Cues For Boundary Correctionmentioning

confidence: 99%

Deep Learning Techniques in Tandem with Signal Processing Cues for Phonetic Segmentation for Text to Speech Synthesis in Indian Languages

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…In-spite of these analyses on resyllabification within the words,specific works on phonetics and phonology of resyllabification across the word boundary not explored much in the implications of TTS. The existence of resyllabification across word boundaries in Indian English (IE) and its significance in building letter-to-sound (LTS) rules for IE is reported in [14]. In [14], Rupak showed that the quality of the synthesized speech of IE TTS system improved with the use of resyllabification rules.…”

Section: Introductionmentioning

confidence: 99%

“…The existence of resyllabification across word boundaries in Indian English (IE) and its significance in building letter-to-sound (LTS) rules for IE is reported in [14]. In [14], Rupak showed that the quality of the synthesized speech of IE TTS system improved with the use of resyllabification rules. However, such studies have not been performed in the context of Indian language TTS systems, where LTS rules have been applied only on isolated words.…”

Section: Introductionmentioning

confidence: 99%

Resyllabification in Indian Languages and Its Implications in Text-to-speech Systems

Mahesh¹,

Prakash

Murthy

2018

Interspeech 2018

View full text Add to dashboard Cite

Resyllabification is a phonological process in continuous speech in which the coda of a syllable is converted into the onset of the following syllable, either in the same word or in the subsequent word. This paper presents an analysis of resyllabification across words in different Indian languages and its implications in Indian language text-to-speech (TTS) synthesis systems. The evidence for resyllabification is evaluated based on the acoustic analysis of a read speech corpus of the corresponding language. This study shows that the resyllabification obeys the maximum onset principle and introduces the notion of prominence resyllabification in Indian languages. This paper finds acoustic evidence for total resyllabification. The resyllabification rules obtained are applied to TTS systems. The correctness of the rules is evaluated quantitatively by comparing the acoustic log-likelihood scores of the speech utterances with the original and resyllabified texts, and by performing a pair comparison (PC) listening test on the synthesized speech output. An improvement in the log-likelihood score with the resyllabified text is observed, and the synthesized speech with the resyllabified text is preferred 3 times over those without resyllabification.

show abstract

“…Syllables are analysed as they are the fundamental units of speech production. Moreover, Indian languages are syllable-timed [8]. Syllables are defined as C * V C * units, where V represents a vowel and C represents a consonant.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Analysis of Syllables Across Indian Languages

Prakash

Murthy

2016

Interspeech 2016

Self Cite

View full text Add to dashboard Cite

Indian languages are broadly classified as Indo-Aryan or Dravidian. The basic set of phones is more or less the same, varying mostly in the phonotactics across languages. There has also been borrowing of sounds and words across languages over time due to intermixing of cultures. Since syllables are fundamental units of speech production and Indian languages are characterised by syllable-timed rhythm, acoustic analysis of syllables has been carried out. In this paper, instances of common and most frequent syllables in continuous speech have been studied across six Indian languages, from both Indo-Aryan and Dravidian language groups. The distributions of acoustic features have been compared across these languages. This kind of analysis is useful for developing speech technologies in a multilingual scenario. Owing to similarities in the languages, text-to-speech (TTS) synthesisers have been developed by segmenting speech data at the phone level using hidden Markov models (HMM) from other languages as initial models. Degradation mean opinion scores and word error rates indicate that the quality of synthesised speech is comparable to that of TTSes developed by segmenting the data using language-specific HMMs.

show abstract

Significance of Pseudo-syllables in building better acoustic models for Indian English TTS

Cited by 7 publications

References 11 publications

Deep Learning Techniques in Tandem with Signal Processing Cues for Phonetic Segmentation for Text to Speech Synthesis in Indian Languages

Deep Learning Techniques in Tandem with Signal Processing Cues for Phonetic Segmentation for Text to Speech Synthesis in Indian Languages

Resyllabification in Indian Languages and Its Implications in Text-to-speech Systems

Acoustic Analysis of Syllables Across Indian Languages

Contact Info

Product

Resources

About