Acoustic Echo and Noise Cancellation System for Hand-Free Telecommunication using Variable Step Size Algorithms

In this paper, acoustic echo cancellation with doubletalk detection system is implemented for a hand-free telecommunication system using Matlab. Here adaptive noise canceller with blind source separation (ANC-BSS) system is proposed to remove both background noise and far-end speaker echo signal in presence of double-talk. During the absence of double-talk, far-end speaker echo signal is cancelled by adaptive echo canceller. Both adaptive noise canceller and adaptive echo canceller are implemented using LMS, NLMS, VSLMS and VSNLMS algorithms. The normalized cross-correlation method is used for double-talk detection. VSNLMS has shown its superiority over all other algorithms both for double-talk and in absence of double-talk. During the absence of double-talk it shows its superiority in terms of increment in ERLE and decrement in misalignment. In presence of double-talk, it shows improvement in SNR of near-end speaker signal

Annotated Speech Corpus for Low Resource Indian Languages: Awadhi, Bhojpuri, Braj and Magahi

In this paper we discuss an in-progress work on the development of a speech corpus for four low-resource Indo-Aryan languages -- Awadhi, Bhojpuri, Braj and Magahi using the field methods of linguistic data collection. The total size of the corpus currently stands at approximately 18 hours (approx. 4-5 hours each language) and it is transcribed and annotated with grammatical information such as part-of-speech tags, morphological features and Universal dependency relationships. We discuss our methodology for data collection in these languages, most of which was done in the middle of the COVID-19 pandemic, with one of the aims being to generate some additional income for low-income groups speaking these languages. In the paper, we also discuss the results of the baseline experiments for automatic speech recognition system in these languages.Comment: Speech for Social Good Workshop, 2022, Interspeech 202

Continuous Density Hidden Markov Model for Hindi Speech Recognition

State of the art automatic speech recognitionsystem uses Mel frequency cepstral coefficients as featureextractor along with Gaussian mixture model for acousticmodeling but there is no standard value to assign number ofmixture component in speech recognition process.Currentchoice of mixture component is arbitrary with littlejustification. Also the standard set for European languagescan not be used in Hindi speech recognition due to mismatchin database size of the languages.Parameter estimation withtoo many or few component may inappropriately estimatethe mixture model. Therefore, number of mixture isimportant for initial estimation of expectation maximizationprocess. In this research work, the authors estimate numberof Gaussian mixture component for Hindi database basedupon the size of vocabulary.Mel frequency cepstral featureand perceptual linear predictive feature along with itsextended variations with delta-delta-delta feature have beenused to evaluate this number based on optimal recognitionscore of the system . Comparitive analysis of recognitionperformance for both the feature extraction methods onmedium size Hindi database is also presented in thispaper.HLDA has been used as feature reduction techniqueand also its impact on the recognition score has beenhighlighted

Discovering Lexical Similarity Using Articulatory Feature-Based Phonetic Edit Distance

Lexical Similarity (LS) between two languages uncovers many interesting linguistic insights such as phylogenetic relationship, mutual intelligibility, common etymology, and loan words. There are various methods through which LS is evaluated. This paper presents a method of Phonetic Edit Distance (PED) that uses a soft comparison of letters using the articulatory features associated with their International Phonetic Alphabet (IPA) transcription. In particular, the comparison between the articulatory features of two letters taken from words belonging to different languages is used to compute the cost of replacement in the inner loop of edit distance computation. As an example, PED gives edit distance of 0.82 between German word ‘vater’ ([fa:tər]) and Persian word ‘ ’ ([pedær]), meaning ‘father,’ and, similarly, PED of 0.93 between Hebrew word ‘ ’ ([ʃəɭam]) and Arabic word ‘ ’ ([səɭa:m], meaning ‘peace,’ whereas classical edit distances would be 4 and 2, respectively. We report the results of systematic experiments conducted on six languages: Arabic, Hindi, Marathi, Persian, Sanskrit, and Urdu. Universal Dependencies (UD) corpora were used to restrict the comparison to lists of words belonging to the same part of speech. The LS based on the average PED between pair of words was then computed for each pair of languages, unveiling similarities otherwise masked by the adoption of different alphabets, grammars, and pronunciations rules

Role of Spectral Peaks in Autocoorelation Domain for Robust Speech Recognition

This paper presents a new front-end for robust speech recognition. This new front-end scenario focuses on the spectral features of the filtered speech signals in the autocorrelation domain. The autocorrelation domain is well known for its pole preserving and noise separation properties. In this paper we will use the autocorrelation domain as an appropriate candidate for robust feature extraction. The proposed method introduces a novel representation of speech for the cases where the speech signal is corrupted by additive noises. In this method, the speech features are computed by reducing additive noise effects via an initial filtering stage, followed by the extraction of autocorrelation spectrum peaks. Robust features based on theses peaks are derived by assuming that the corrupting noise is stationary in nature. A task of speaker-independent isolated-word recognition is used to demonstrate the efficiency of these robust features. The cases of white noise and colored noise such as factory, babble and F16 are tested. Experimental results show significant improvement in comparison to the results obtained using traditional front end methods. Further enhancement has been done by applying cepstral mean normalization (CMN) on the above extracted features

The Effect of Bilingual Proficiency in Indian English on Bilabial Plosive

Background: Bilingual speech production studies have highlighted that level of proficiency influences the acoustic-phonetic representation of phonemes in both languages (MacKay, Flege, Piske, & Schirru 2001; Zárate-Sández, 2015). The results for bilingual speech production reveal that proficient/early bilinguals produce distinct acoustic properties for the same phoneme in each language, whereas less proficient/late bilinguals produce acoustic properties for a phoneme that is closer to the native language (Flege et al., 2003; Fowler et al., 2008). Acoustic-phonetic studies for Hindi (L1) and Indian English (L2) for bilingual speakers have been understudied, and the level of proficiency has not been considered in Hindi and Indian English bilingual speakers. The present study aimed to measure the acoustic differences produced by bilingual speakers of varying proficiencies for Indian English on bilabial plosive and determine how the bilabial plosives are different from American English bilabial plosives. Methods: The sample size for this study was twenty-four. However, only twenty participants (eleven females) between the ages of eighteen and fifty, with normal speech and hearing, were recruited. The lack of recruitment of four more participants was due to the inability to find bilingual speakers who spoke Hindi as their first language and Indian English as their second language and COVID-19 restrictions imposed on recruitment (n=4). The participants were divided into three groups based on language and proficiency: a monolingual American English group, a proficient bilingual Hindi-Indian English group, and a less-proficient bilingual Hindi-Indian English group. The bilinguals were divided into a proficient and less proficient group based on the Language Experience and Proficiency Questionnaire (Marian, Blumenfeld, & Kaushanskaya, 2007). Following the screening, participants took part in a Nonword Repetition Task. Data were analyzed using Praat and Voice Sauce software. A linear mixed-effects model using R statistics was used for the statistical analysis. Results: Data from 20 participants (seven proficient bilingual speakers, five less-proficient bilingual speakers, and eight monolingual speakers) were included in the data analysis. Approximately four thousand repetitions were evaluated across the remaining participants. There were no significant main effects across the four dependent variables, but there was an interaction effect between group and phoneme on two dependent variables. The closure duration for proficient bilingual speakers compared to less-proficient bilingual speakers were significantly different between the voiceless unaspirated bilabial plosive (VLE) and voiceless aspirated bilabial plosive (VLH), as well as voiced unaspirated bilabial plosive (VE) and voiced aspirated bilabial plosive (VH). For spectral tilt, there was a significant difference between the VLE and VLH for proficient bilingual speakers compared to less proficient bilingual speakers. Discussion: The results of this study suggest that proficient bilingual speakers have a faster rate of speech in both their first language and second language. Therefore, it is difficult to provide information on whether this group has separate acoustic-phonetic characteristics for each phoneme for each language. In contrast, the less-proficient bilingual speakers seem to have a unidirectional relationship (i.e., first language influences the second language). Furthermore, the results of the acoustic characteristics for the control group i.e., monolingual American English speakers suggest that they may have acoustic-phonetic characteristics that represent a single acoustic-phonetic representation of bilabial plosive with their voicing contrast