Dialectal influences on acoustic duration of Hindi phonemes

Sinha, Shweta; Agrawal, S.; Jain, Aruna

doi:10.1109/icsda.2013.6709866

Cited by 8 publications

(6 citation statements)

References 4 publications

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“…A couple of works have been performed for the recognition of the spoken dialect. The majority of them are based on the phonotactic research study [33], [34], [35]. Some works based upon acoustic features have been carried out for automated identification of spoken dialect for languages in western countries.…”

Section: Introductionmentioning

confidence: 99%

Robust Feature Extraction Based on Spectral and Prosodic Features for Classical Arabic Accents Recognition

Ibrahim

Idris

Rahman

et al. 2019

MJCS

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The variability of speech patterns produced by individuals is unique. The uniqueness is due to the accent influenced by the individual’s native dialect. Modeling individual variation of spoken language is a challenge under the Automatic Speech Recognition (ASR) field. The individual differences concerning of accent revealed the critical issues in Classical Arabic (CA) recitation among Malay speakers. This problem is caused by the misarticulate phonemes, which affected by the Malay colloquial dialect and native language. Most of ASR researchers are unable to understand the behavior of phonemes and speech patterns in CA, thus degrading the ASR performance. This paper focuses on identifying the accent of Malay speakers on the recitation of Sūrah Al-Fātiḥah with 7 Quranic accents, using the proposed feature extraction technique. In this work, the technique presented is a combination of spectral and prosodic features, which are mainly designed for accent in ASR. Differed with current conventional method, where the spectral feature alone has been applied for feature extraction in many ASR research. The prosodic elements in CA such as pitch, energy and spectral-tilt need to be taken into consideration, thus a significant variety of features for each phoneme able to help in distinguishing one accent from another. Meanwhile, the spectral representation of Mel-Frequency Cepstral Coefficients (MFCC) is utilized for the decorrelating property of the cepstrum. At present, Gaussian Mixture Models (GMM) has been applied for the classification stage. From experimental results, the system performance is the best when the prosodic is integrated with MFCC, alongside the GMM with 81.7%-89.6% of accuracy. It was 5.5%-7.3% increment as compared to MFCC alone.

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Section: Introductionmentioning

confidence: 99%

Robust Feature Extraction Based on Spectral and Prosodic Features for Classical Arabic Accents Recognition

Ibrahim

Idris

Rahman

et al. 2019

MJCS

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“…Most of the literature available focuses on analysing acoustic properties of individual Indian languages [1][2][3]. Similarities among different languages from a language identification perspective are studied in reference [4].…”

Section: Introductionmentioning

confidence: 99%

“…Similarities among different languages from a language identification perspective are studied in reference [4]. From a multilingual perspective to better design speech technologies, reference [5] analyses subtle variations in phonetic features across multiple 1 This figure is from the 1961 Census report of India languages. In contrast to this, reference [6] exploits the similarities that exist among Indian languages to design a common label set for building multiple text-to-speech (TTS) synthesisers.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Analysis of Syllables Across Indian Languages

Prakash

Murthy

2016

Interspeech 2016

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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.

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“…Phonotactic models are based on phone sequence distribution, where vowel inventory, tense marking, diphthong formation, etc. are the base for the study [3,4]. Dialect recognition is analogous to language identification (LID) task.…”

Section: Introductionmentioning

confidence: 99%

“…are the base for the study [3,4]. Dialect recognition is analogous to language identification (LID) task.…”

Section: Introductionmentioning

confidence: 99%

Acoustic-Phonetic Feature Based Dialect Identification in Hindi Speech

Sinha

Jain

Agrawal³

2015

International Journal on Smart Sensing and Intelligent Systems

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Abstract-Every individual has some unique speaking style and this variation influences their speech characteristics. Speakers' native dialect is one of the major factors influencing their speech characteristics that influence the performance of automatic speech recognition system (ASR). In this paper, we describe a method to identify Hindi dialects and examine the contribution of different acoustic-phonetic features for the purpose. Mel frequency cepstral coefficients (MFCC), Perceptual linear prediction coefficients (PLP) and PLP derived from Mel-scale filter bank (MF-PLP) have been extracted as spectral features from the spoken utterances. They are further used to measure the capability of Auto-associative neural networks (AANN) for capturing non-linear relation specific to information from spectral features. Prosodic features are for capturing longrange features. Based on these features efficiency of AANN is measured to model intrinsic characteristics of speech features due to dialects.

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Dialectal influences on acoustic duration of Hindi phonemes

Cited by 8 publications

References 4 publications

Robust Feature Extraction Based on Spectral and Prosodic Features for Classical Arabic Accents Recognition

Robust Feature Extraction Based on Spectral and Prosodic Features for Classical Arabic Accents Recognition

Acoustic Analysis of Syllables Across Indian Languages

Acoustic-Phonetic Feature Based Dialect Identification in Hindi Speech

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