Pseudo-Labeling for Domain-Agnostic B angla Automatic Speech Recognition (2023)

Abstract: One of the major challenges for developing automatic speech recognition (ASR) for low-resource languages is the limited access to labeled data with domain-specific variations. In this study, we propose a pseudo-labeling approach to develop a large-scale domain-agnostic ASR dataset. With the proposed methodology, we developed a 20k+ hours labeled Bangla speech dataset covering diverse topics, speaking styles, dialects, noisy environments, and conversational scenarios. We then exploited the developed corpus to design a conformer-based ASR system. We benchmarked the trained ASR with publicly available datasets and compared it with other available models. To investigate the efficacy, we designed and developed a human-annotated domain-agnostic test set composed of news, telephony, and conversational data among others. Our results demonstrate the efficacy of the model trained on psuedo-label data for the designed test-set along with publicly-available Bangla datasets. The experimental resources will be publicly available.https://github.com/hishab-nlp/Pseudo-Labeling-for-Domain-Agnostic-Bangla-ASR

## introduction modern end-to-end automatic speech recognition (e2e-asr) systems have made remarkable strides, performing well across various types of data @xcite @xcite . this success can be attributed to the advancement of deep learning techniques relying on different training strategies, highly dependent on large datasets. however, acquiring and maintaining these high-quality human transcriptions is both expensive and timeconsuming, and hence hinders further progress for asr especially in low-resource languages like bangla. to overcome these challenges, two dominant methods, leveraging unlabeled audio, are gaining popularity. these methods include: (i) pre-training via self-supervised learning (ssl) @xcite @xcite ; despite being the 6 th most widely spoken language globally, bangla still falls under low resource language family mainly due to the lack of accessible open datasets. to reduce this gap, we introduce a pseudo-labeling approach to develop an extensive, large-scale, and high-quality speech dataset of ≈ 20, 000 hours for developing domain-agnostic bangla asr. first, we curated and cleaned the largest collection of bangla audio-video data from various bangla tv channels on youtube (yt)varying domains, speaking styles, dialects, and communication channels among others. we then leverage the alignments from two asr systems, to segment and automatically annotate the audio segments. we enrich the quality of pseudo-labels with our confidence and duration-based filtering method. we utilize the created dataset to design an end-to-end state-of-the-art bangla asr. finally, we benchmark the asr with widely used, domainagnostic test sets and compare it with both publicly and commercially available bangla asr systems. to test domain-generalization capability, we also developed manually annotated test sets that include domain-diverse speech segments. our contributions are as follows: the rest of the paper is organized as follows: section 2 presents previous work, section 3 describes the dataset, section 4 formulates our experiments, section 5 discusses the evaluation results. finally, section 6 concludes and points to possible directions for future work. ## dataset 3.1 data collection to develop a large-scale dataset focused on diverse domains, we selected youtube as our data source due to its extensive coverage of bangla speech. we gathered content from popular news channels such as atn news, banglavision news, zee 24 ghanta, news18bangla, republic bangla, dd bangla news, abp ananda, ntv news, dbc news, bbc news bangla, channel 24, mytvbd news, news24, and channel i news, among others. additionally, we included talk shows like rtv talkshow and atn bangla talk show. we have also incorporated travel vlogs into our dataset. crawler: to facilitate the collection of data from youtube, we developed a web crawler that periodically collects videos using youtube-dl. 3 this crawler operates on a list of youtube channels that we manually pre-select to ensure domain diversity. the crawler then lists all available videos from each channel and proceeds to download them. the download module within the crawler stores the downloaded videos in a google cloud storage (gcs) bucket. the resulting collection consists of ∼53k hours with 42k number of videos. ## conclusion and future work this study offers a significant contribution in bangla speech processing, in addition to the field of asr particularly for low-resource language. the primary contribution of this paper lies in demonstrating that the model trained with pseudo-labeling only, offers comparable performance with supervised asr systems. specifically, the megabn-speech model excels in their ability to generalize across multiple domains and channels as shown in the results. additionally, the developed train, development, and two test sets of megabnspeech corpus of ≈ 20, 000 hours of data will serve as a valuable resource for the research community. the megab-nspeech corpus, especially the manually annotated yt and telephony test sets, can be used as a benchmark for future studies, enabling other researchers to build upon our work and potentially discover even more effective methods for designing lowresource asr.