A syllable-based framework for unit selection synthesis in 13 Indian languages

2014 International Conference on Asian Language Processing (IALP)

2014

Phonetic segmentation plays a key role in developing various speech applications. In this work, we propose to use various features for automatic phonetic segmentation task for forced Viterbi alignment and compare their effectiveness. We propose to use novel multiscale fractal dimension-based features concatenated with MelFrequency Cepstral Coefficients (MFCC). The novel features are expected to capture additional nonlinearities in speech production which should improve the performance of segmentation task. However, to evaluate effectiveness of these segmentation algorithms, we require manual accurate phoneme-level labeled data which is not available for low resource languages such as Gujarati (a low resource language and one of the official languages of India). In order to measure effectiveness of various segmentation algorithms, HMM-based speech synthesis system (HTS) for Gujarati have been built. From the subjective and objective evaluations, it is observed that FD-based features for segmentation work moderately better than other state-ofthe-art features such as MFCC, Perceptual Linear Prediction Cepstral Coefficients (PLP-CC), Cochlear Filter Cepstral Coefficients (CFCC), and RelAtive SpecTrAl(RASTA)-based PLP-CC. The Mean Opinion Score (MOS) and the Degraded-MOS, which are the measures of naturalness indicate an improvement of 9.69% with the proposed features from the MFCC (which is found to be the best among the other features) based features.

Section: B Adaptation To Gujarati Languagementioning

confidence: 99%

Effectiveness of multiscale fractal dimension-based phonetic segmentation in speech synthesis for low resource language

Zaki

2014 International Conference on Asian Language Processing (IALP)

2014

“…Out of which some of the sentences have been taken for speech recordings. Then, from one female and one male (who are professional voice over artists), we have recorded sentences from professional studio in Ahmedabad [15].…”

Section: A Gujarati Speech Corpus Developmentmentioning

confidence: 99%

Influence of various asymmetrical contextual factors for TTS in a low resource language

Zaki

2014 International Conference on Asian Language Processing (IALP)

2014

The generalized statistical framework of Hidden Markov Model (HMM) has been successfully applied from the field of speech recognition to speech synthesis. In this paper, we have applied HMM-based Speech Synthesis (HTS) method to Gujarati (one of the official languages of India). Adaption and evaluation of HTS for Gujarati language has been done here. In addition, to understand the influence of asymmetrical contextual factors on quality of synthesized speech, we have conducted series of experiments. Evaluation of different HTS built for Gujarati speech using various asymmetrical contextual factors is done in terms of naturalness and speech intelligibility. From the experimental results, it is evident that when more weightage is given to left phoneme in asymmetrical contextual factor, HTS performance improves compared to conventional symmetrical contextual factors for both triphone and pentaphone case. Furthermore, we achieved best performance for Gujarati HTS with left-left-left-centre-right (i.e., LLLCR) contextual factors.

“…We have collected optimized text in UTF-8 format. The sentences have been recorded by one female and one male (professional voice over artist) from professional studio [21]. Except, context-dependent modeling, every block of HTS is language-independent.…”

Section: Adaptation Of Hts To Gujaratimentioning

confidence: 99%

Deterministic annealing EM algorithm for developing TTS system in Gujarati

The 9th International Symposium on Chinese Spoken Language Processing

Madhavi

et al. 2014

The generalized statistical framework of Hidden Markov Model (HMM) has been successfully applied from the field of speech recognition to speech synthesis. In this work, we have applied HMM-based Speech Synthesis System (HTS) method to Gujarati language. Adaption and evaluation of HTS for Gujarati language has been done here. Evaluation of HTS system built using Gujarati data is done in terms of naturalness and speech intelligibility. Apart from this, a conventional EM algorithm-based HTS and recently proposed Deterministic Annealing EM algorithm-based HTS has been applied to Gujarati (a low resourced language) and it's relative comparison has been done. It has been found that HTS in Gujarati has very high intelligibility. It was verified from ABtest that 70.5 % times DAEM-based HTS has preferred over EM-based HTS developed for Gujarati language.