Abstract

The task of automatically detecting syllable stress is a key module in computer-assisted language learning systems. There are numerous studies proposed in the literature for automatic syllable stress detection by using different knowledge-based prosodic features. Also, different statistical machine learning and deep learning models are explored for this task using knowledge-based features. However, the acoustic parameters considered to compute knowledge-based features might not always represent the stress phenomena, hence the knowledge based features are not always suitable for generalization and scalability. Recently, the rapidly emerging self-supervised learning based representations are outperforming the existing state-of-the-art knowledge-based features in all speech applications. Also, these representations allow the models to be built in an end-to-end fashion. In this work, we explore the use of self-supervised representations (Wav2Vec-2.0), for syllable stress detection and compare the performance with state-of-the-art knowledge-based features. Further, we use our recently proposed explicit representation learning framework, modeled by jointly optimizing variational autoencoder (VAE) and DNN for stress detection. We analyze the performance of representation learning framework with two different state-of-the-art classifiers, support vector machines (SVM) and simple deep neural network (DNN). We conduct experiments on two non-native English speakers’ datasets from ISLE corpus i.e., German (GER), and Italian (ITA). From the analysis study, it is observed that the classification accuracy for syllable stress detection using self-supervised representations significantly improved by 3.2% and 2.7% over knowledge-based features in GER and ITA, respectively. From the t-SNE plots, it is observed that the representations learned by explicit representation learning framework with VAE show better discrimination among stressed and unstressed syllables compared with representations learned implicitly with simple DNN.

Abstract

Speech intelligibility is crucial in language learning for effective communication. Thus, to develop computer-assisted language learning systems, automatic speech intelligibility detection (SID) is necessary. Most of the works have assessed the intelligibility in a supervised manner considering manual annotations, which requires cost and time; hence scalability is limited. To overcome these, this work proposes an unsupervised approach for SID. The proposed approach considers alignment distance computed with dynamic-time warping (DTW) between teacher and learner representation sequence as a measure to separate intelligible versus non-intelligible speech. We obtain the feature sequence using current state-of-the-art self-supervised representations from Wav2Vec-2.0. We found the detection accuracies as 90.37\%, 92.57\% and 96.58\%, respectively, with three alignment distance measures -- mean absolute error, mean squared error and cosine distance (equal to one minus cosine similarity).

Abstract

Language learning involves the correct acquisition of grammar skills. To facilitate learning with computer-assisted systems, automatic spoken grammatical error detection (SGED) is necessary. This work explores Automatic Speech Recognition (ASR), which decodes text from speech, for SGED. With current advancements in ASR technology, often it can be believed that these systems could capture spoken grammatical errors in the decoded text. However, these systems have an inherent bias from the language model towards the grammatically correct text. We explore the ASR-decoded text from commercially available current state-of-the-art systems considering a text-based GED algorithm and also its word-level confidence score (CS) for SGED. We perform the experiments on the spoken English data collected in-house from 13 subjects speaking 4110 grammatically erroneous and correct sentences. We found the highest relative improvement in SGED with CS is 15.36% compared to that with decoded text plus GED.

Abstract

The pronunciation quality of second language (L2) learners can be affected by different factors including the fol- lowing seven factors: Intelligibility, Intonation, Phoneme quality, mispronunciation, Mother tongue influence, Correct placement of pause and Correct stress placement. An automatic assessment of these seven factors could be helpful for developing computer- assisted language learning systems. In this work, we assess the quality of all seven factors considering an unsupervised approach using DTW based utterance level alignment distance between expert’s and learner’s speech. Unlike the existing works that consider factor specific heuristic based features, we explore Wav2Vec-2.0 based self-supervised representations as a feature for assessing all the seven factors. The distance is computed using the following three distance metrics: Mean absolute error (MAE), Mean squared error (MSE) and Cosine distance (CD). Experiments are conducted on voisTUTOR corpus containing spoken English speech samples from 16 Indian L2 learners annotated with binary ratings (1 and 0) for all the factors. Using each distance metric, for each learner’s speech, four distance values are computed with a set of four expert samples. In the assessment, to circumvent the need for parallel expert data, we consider two (out of four) expert samples synthesized from the state-of-the-art text-to-speech (TTS) systems. We observe that the performance with the considered distance metric based unsupervised assessment approach is significantly more than that with the baseline for six out of seven factors under all three distance metrics and all four experts samples.

Abstract

The demand for high-quality speech data has been increasing as deep-learning approaches gain popularity in speech applications. Among these, automatic speech recognition (ASR) and text-to-speech (TTS) require large amount of data contain- ing speech and the corresponding text. For these applications, high-quality data is often obtained through manual validation, which ensures matching between speech and text. The manual validation is not scalable as per the demand due to the cost and time involved. In order to cater to the high-quality data demand, validating the data automatically could be useful. In this work, for automatic data validation, a spoken English corpus named IIITH MM2 Speech-Text is created, containing matched and mismatched speech-text pairs under read speech conditions from Indian speakers with different nativities. For the creation, we consider 100 unique stimuli selected from the TIMIT corpus, ensuring phonetic richness, for which a joint entropy maximization is proposed. These stimuli are recorded from 50 speakers, resulting in matched and mismatched sets containing 5000 and 764 utterances with a total duration of 6 hours and 1 hour, respectively. The mismatched set contains speech from the instances where the speakers naturally made spoken errors while reading the reference text. It also contains two stimuli per utterance, one stimulus is the reference text, and the other is manually annotated text that reflects the erroneous speech. Thus, the reference and the annotated text are used for building the models of speech-text mismatch detection and correction, respectively. To the best of our knowledge, no such corpora exist containing both matched and mismatched speech-text. As a preliminary analysis for speech-text mismatch detection, a baseline considering Wav2Vec-2.0 representations and DTW results in the detection F1-score of 0.87

Abstract

Second language learners often make grammatical mistakes, which can impact the performance of Spoken Language Understanding (SLU) systems. SLU systems consist of two key components: Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU). This work focuses on the effects of grammatical errors which were synthetically generated by manipulating Parts of Speech (POS) tokens on the NLU module. Our analysis comprises three main aspects. Firstly, we assess the impact of grammatical errors on the overall performance of NLU systems, specifically in the domains of Intent Detection and Slot Filling. Secondly, we investigate NLU performance concerning POS tags. Lastly, we utilize an Attention-based NLU model to evaluate the significance of different POS. This study evaluates NLU models on ATIS and SNIPS datasets for Intent Detection and Slot Filling tasks with introduced grammatical errors, leading to a significant performance drop as large as 4.7% for Intent Detection and 5.1% for Slot Filling. However, training on datasets with synthetic errors helps mitigate this drop, and the models are shown to be sensitive to increasing levels of corrupted tokens. Additionally, attention analysis reveals Proper Nouns’ higher weights in determining intent, while specific POS corruption, particularly Auxiliary Verbs and Nouns, is more likely to cause intent misclassification.

Abstract

Analysis of Indian English (IE) pronunciation variabilities is useful in ASR and TTS modelling for the Indian context. Prior works characterised IE variabilities by reporting qualitative phonetic rules relative to Received Pronunciation (RP). However, such characterisations lack quantitative descriptors and data-driven analysis of diverse IE pronunciations, which could be due to the scarcity of phonetically labelled data. Furthermore, the versatility of IE stems from the influence of a large diversity of the speakers’ mother tongues and demographic region differences. To address these issues, we consider the corpus Indic TIMIT and manually obtain 13, 632 phonetic transcriptions in addition to those parts of the corpus. By performing a data-driven analysis on 15, 974 phonetic transcriptions of 80 speakers from diverse regions of India, we present a new set of phonetic rules and validate them against the existing phonetic rules to identify their relevance. Finally, we test the efficacy of Grapheme-to-Phoneme (G2P) conversion developed based on the obtained rules considering Phoneme Error Rate (PER) as the metric for performance.

Abstract

Automatic syllable stress detection is helpful in assessing L2 learners’ pronunciation. In this work, for stress detection, we propose a representation learning framework by jointly optimizing VAE and DNN. The obtained representations from the proposed VAE plus DNN framework are compared with the implicit representations learned from DNN-based stress detection. Further, we compare the obtained representations from VAE plus DNN with those obtained from autoencoder (AE) plus DNN, and sparse-autoencoder (SAE) plus DNN considering with/without implicit representations from DNN. We perform the experiments on the ISLE corpus consisting of English utterances from German and Italian native speakers. We observe that the detection performance with the learned representations from VAE plus DNN is significantly better than that with the state-of-the-art method without any representation learning with the highest improvement of 2.2%, 5.1%, and 1.4% under matched, combined, and cross scenarios, respectively.

Abstract

Typically, automatic syllable stress detection is posed as a supervised classification problem, for which, a classifier is trained using manually annotated (existing) syllable data and stress labels. However, in real testing scenarios, syllable data is estimated since manual annotation is not possible. Further, the estimation process could result in a mismatch between the lengths of the estimated and the existing syllable data causing no one- to-one correspondence between the estimated syllable data and the existing labels. Hence, the existing labels and estimated syllable data together cannot be used to train the classifier. This can be avoided by manually labeling the estimated syllable data, which, however, is impractical. In contrast, we, in this work, propose a method to obtain labels for estimated syllable data without using manual annotation. The proposed method considers a weighted version of the well-known Wagner–Fisher algorithm (WFA) to assign the existing labels to the estimated syllable data, where the weights are computed based on a set of three constraints defined in the proposed algorithm. Experiments on ISLE corpus show that the performance obtained on the test set for four different types of estimated syllable data are higher when the assigned labels and estimated syllable data are used for training compared to those when existing labels and existing syllable data are used. Also, the label assignment accuracy using the proposed method is found to be higher than that using a baseline scheme based on WFA.

Abstract

Speech systems are sensitive to accent variations. This is especially challenging in the Indian context, with an abundance of languages but a dearth of linguistic studies characterising pronunciation variations. The growing number of L2 English speakers in India reinforces the need to study accents and L1-L2 interactions. We investigate the accents of Indian English (IE) speakers and report in detail our observations, both specific and common to all regions. In particular, we observe the phonemic variations and phonotactics occurring in the speakers' native languages and apply this to their English pronunciations. We demonstrate the influence of 18 Indian languages on IE by comparing the native language pronunciations with IE pronunciations obtained jointly from existing literature studies and phonetically annotated speech of 80 speakers. Consequently, we are able to validate the intuitions of Indian language influences on IE pronunciations by justifying pronunciation rules from the perspective of Indian language phonology. We obtain a comprehensive description in terms of universal and region-specific characteristics of IE, which facilitates accent conversion and adaptation of existing ASR and TTS systems to different Indian accents.