Under-resourced dialect identification in Ao using source information.

This paper reports the findings of an automatic dialect identification (DID) task conducted on Ao speech data using source features. Considering that Ao is a tone language, in this study for DID, the gammatonegram of the linear prediction residual is proposed as a feature. As Ao is an under-resourced language, data augmentation was carried out to increase the size of the speech corpus. The results showed that data augmentation improved DID by 14%. A perception test conducted on Ao speakers showed better DID by the subjects when utterance duration was 3 s. Accordingly, automatic DID was conducted on utterances of various duration. A baseline DID system with the Slms feature attained an average F1-score of 53.84% in a 3 s long utterance. Inclusion of source features, Silpr and S, improved the F1-score to 60.69%. In a final system, with a combination of Silpr, S, Slms, and Mel frequency cepstral coefficient features, the F1-score increased to 61.46%.

Analysis and modeling of dialect information in Ao, a low resource language.

Ao is a Tibeto-Burman language spoken in Nagaland, India. It is a low resource, tonal language with three lexical tones, namely, high, mid, and low. However, tone assignment on lexical words may differ among the three dialects of Ao, namely, Chungli, Mongsen, and Changki. In this work, an acoustic study is conducted on the three tones in the three dialects of Ao. It was found that the acoustic characteristics of the tones in the Changki dialect are markedly different from that of the Chungli and the Mongsen dialects. Hence, in the latter part of the work, automatic dialect identification (DID) in the Ao dialects is attempted with Mel Frequency Cepstral Coefficients, Shifted Delta Cepstral coefficients, and F0 features using the Gaussian Mixture models. It is confirmed that in both text-dependent and text-independent DID, the F0 features improve the accuracy of classification.

Acoustic phonetic study of the Sora vowel system.

This paper is an acoustic phonetic study of vowels in Sora, a Munda language of the Austroasiatic language family. Descriptions here illustrate that the Sora vowel system has six vowels and provide evidence that Sora disyllables have prominence on the second syllable. While the acoustic categorization of vowels is based on formant frequencies, the presence of prominence on the second syllable is shown through temporal features of vowels, including duration, intensity, and fundamental frequency. Additionally, this paper demonstrates that acoustic categorization of vowels in Sora is better in the prominent syllable than in the non-prominent syllable, providing evidence that syllable prominence and vowel quality are correlated in Sora. These acoustic properties of Sora vowels are discussed in relation to the existing debates on vowels and patterns of syllable prominence in Munda languages of India. In this regard, it is noteworthy that Munda languages, in general, lack instrumental studies, and therefore this paper presents significant findings that are undocumented in other Munda languages. These acoustic studies are supported by exploratory statistical modeling and statistical classification methods.

Aspiration in fricative and nasal consonants: Properties and detection.

Unlike aspiration in stops, occurrence of aspiration in non-stop consonants is quite rare. Most of the languages that have aspirated non-stop consonants are low-resource languages. Hence, data driven, quantitative, and statistical analysis of their aspiration phenomena is fairly limited. Rabha and Angami are considered in this study, as previous studies have confirmed the existence of aspiration contrast in fricatives and nasals. This study reports the acoustic characteristics of aspiration in stops, fricatives, and nasals. Among them, distinguishing the aspirated fricatives and aspirated nasals from their unaspirated counterparts is a challenging task. A set of acoustic features is proposed to automatically detect the presence of aspiration in fricatives and nasals. Acoustic features, such as vocal tract constriction (VTC), normalized autocorrelation peak strength (NAPS), strength of excitation (SoE), and variance of successive epoch intervals (VSEI) are used to detect aspiration in fricatives and nasals. These features are extracted from zero-frequency filtered signal of the speech sounds, as it preserves the aspiration information. Results show that VTC, NAPS, and SoE can detect aspiration in nasals, whereas SoE and VSEI can detect aspiration in fricatives. The proposed method improves the performance of an automatic phoneme recognizer by reducing the confusion between aspirated and unaspirated counterparts.