Non-native Accent Partitioning for Speakers of I ndian Regional Languages (2019)

Abstract: Acoustic features extracted from the speech signal can help in identifying speaker related multiple information such as geographical origin, regional accent and nativity. In this paper, classification of native speakers of South Indian languages is carried out based upon the accent of their non-native language, i.e., English. Four South Indian languages: Kannada, Malayalam, Tamil, and Telugu are examined. A database of English speech from the native speakers of these languages, along with the native language speech data was collected, from a non-overlapping set of speakers. Segment level acoustic features F0 and Mel-frequency cepstral coefficients (MFCCs) are used. Accent partitioning of non-native English speech data is carried out using multiple classifiers: k-nearest neighbour (KNN), linear discriminant analysis (LDA) and support vector machine (SVM), for validation and comparison of results. Classification accuracies of 86.6% are observed using KNN, and 89.2% or more than 90% using SVM classifier. A study of acoustic feature F0 contour, related to L2 intonation, showed that native speakers of Kannada language are quite distinct as compared to those of Tamil or Telugu languages. It is also observed that identification of Malayalam and Kannada speakers from their English speech accent is relatively easier than Telugu or Tamil speakers.

## introduction identification of speakers, classification of their dialectal zones is important in a multilingual country like india @xcite . speaker uniqueness is manifested in both anatomical and learned traits. when the context is constrained, speaker characteristics can be used reliably to identify individuals @xcite . the accent is one of the glaring indications of linguistic and social background of a speaker. studying the characteristics of dialect on a phonetic or phonemic level belongs to accent recognition @xcite . earlier studies have concluded that native language (l 1 ) affects the speaker's traits of their second language (l 2 ) @xcite . analysis and classification of utterances that belong to specific groups of learners is the main objective of native language identification (nli) @xcite . however, there is very little research on the question of accuracy with which accent features can be used to identify a speaker's regional or ethnic origin @xcite . a solution to the problem of regional accent classification across english speaking south indians is attempted in the present research, using a specifically developed corpus. discriminative classifiers based on characterizing acoustic differences across foreign accents can be employed to direct an accent dependent recognition system @xcite . systems with an automatic evaluation of non-native speech, which includes characteristics of the mother tongue will have better performance over similar algorithms that depend upon target languages @xcite . this is particularly true when the text uttered is unknown. native listeners are mostly aware of the speaker's regional accent and also the social or geographical subgroup within the region @xcite . automatic speaker characterization is vital in real-world applications and the advantages are widely open @xcite . pattern recognition approach of collecting data, extracting suitable features, and training classification module using machine learning is a powerful tool in applications like computer-assisted-pronunciation-training (capt) programs. acoustic descriptors are critical in tasks such as sound classification @xcite . state-of-the-art accent identification (aid) systems widely rely on spectral acoustic distribution for modeling the pronunciation. in applications like accent recognition, features distinguishing different phonemes of a language will be useful @xcite . languagespecific differences in phonological development might be related to differences in phoneme and phoneme sequence frequency across languages @xcite . such variations are also represented by the intonation patterns of individuals @xcite . apart from cepstral features that capture underlying acoustic characteristics, information from higher-level prosodic traits @xcite were examined in the present study. english is the most widely spoken second language in india and elsewhere in the world @xcite . indian english has several varieties with their specific accents and phonological features and often a distinct lexicon. research on spoken english of indian speakers is urgently needed from a multidisciplinary perspective @xcite . present work is aimed at comparing the acoustic properties that are likely to differ between english accents different groups of south indian language of speakers. the nonnative prosodic traits are a hindrance to proficiency in a second language (l 2 ), and also to the mutual understanding. present work also examines the local prosodic changes in the non-native english speech, without incorporating any phonol-ogy of the specific languages. the ability to compensate against prosodic deviation during english production can be improved by identifying the articulatory gestures that emphasize the non-native speaker accent. the paper is organized as follows: section 2 presents the details of the database, including the recording methodology. section 3 describes acoustic and prosodic features used in foreign accent recognition. section 4 describes the classification procedures employed in the nli experiments. section 5 gives the details of the experiments and results. analysis of results of regional accent classification is given in section 6. section 7 describes the key outcome and contributions. conclusions drawn are given in section 8. the main focus of current research work is on differentiating the regional non-native english accents of speakers, and also describing foreign accent in terms of a common set of fundamental speech attributes. a database has been specifically developed (g. @xcite with native and non-native speech samples containing utterance by the speakers belonging to language groups kannada (kan), malayalam (mal), tamil (tam), and telugu (tel). ## features for non-native accent partitioning understanding similar variations in foreign accents is a crucial factor for the development of an nli system. the dominant articulatory traits of different languages are different @xcite . in applications like accent recognition, features distinguishing different phonemes of a language will be useful @xcite . the acoustic signature or the voice individuality of the speech signal are available as differences in transformations occurring at semantic, linguistic articulatory, and acoustic levels. out of all the factors affecting speech, accent is a week factor in the sense that speech variation is not as evident as that due to speaker/gender. language-specific differences in phonological development might be related to differences in phoneme and phoneme sequence frequency across languages @xcite . speakers of the second language (sl) are expected to import certain patterns from their native language (nl) which are audible in sl. the influence of the surrounding speech prosody on new-born cry melody has been shown (monnin and loevenbruck, 2010). the non-native speech detection is thus very challenging . characterization of a foreign accent is mostly based on either auditory analysis or manual transcriptions of deviations. the auditory spectrum is consistent with several phenomena observed in speech perception and is useful in automatic speaker independent speech recognition. features used for nonnativeness detection include cepstral vectors, phone strings and a variety of prosodic features, but when used alone, systems based on acoustic features perform better @xcite . we can consider acoustic features, which are proxy of phonetic reproduction as acousticphonetic features @xcite . ## classification for non-native accent partitioning speaker classification can be conveniently defined as a grouping of speakers speaking in a similar manner, on the basis of acoustic characteristics @xcite . classification of foreign accents directly from the acoustic features is atfigure 7: distribution of mfcc coefficients c 0 versus c 1 for english speech by telugu speakers ## key outcome and contributions ## conclusion it can be concluded that the regional native language classification has been achieved with an accuracy of nearly 90%, by using the acoustic distribution of cepstral features on the four types of non-native south indian english speech. it is known that systems make more mistakes among regionally close languages. accent differences among the non-native speakers are reflected as the deviation of l 2 influenced by l 1 on prosodic level. studies carried out based on intonation distribution indicates that english speaking south indian groups corresponding to kannada, malayalam, tamil, and telugu are clearly divided as per their native languages. prosodic differences in the native and english speech by south indian speakers were detected without annotation. present method can potentially be applied to other languages like hindi, and in addressing the important question of finding a universal feature set for identifying the non-native speech. present research is useful in applications such as voice based wireless services like mobile health care, agriculture. automatic accent characterization can also be applied to fields such as sociolinguistics and speech pathology. future work can employ different speech styles, and characteristics of speaker population to be carefully scrutinized, and also by including multi-disciplinary information. further, the results can be extended to separating language families and also for rating l 2 proficiency.