Sessizliğin Kaldırılması ve Konuşmanın Parçalara Ayrılması İşleminin Türkçe Otomatik Konuşma Tanıma Üzerindeki Etkisi

Otomatik Konuşma Tanıma sistemleri temel olarak akustik bilgiden faydalanılarak geliştirilmektedir. Akustik bilgiden fonem bilgisinin elde edilmesi için eşleştirilmiş konuşma ve metin verileri kullanılmaktadır. Bu veriler ile eğitilen akustik modeller gerçek hayattaki bütün akustik bilgiyi modelleyememektedir. Bu nedenle belirli ön işlemlerin yapılması ve otomatik konuşma tanıma sistemlerinin başarımını düşürecek akustik bilgilerin ortadan kaldırılması gerekmektedir. Bu çalışmada konuşma içerisinde geçen sessizliklerin kaldırılması için bir yöntem önerilmiştir. Önerilen yöntemin amacı sessizlik bilgisinin ortadan kaldırılması ve akustik bilgide uzun bağımlılıklar sağlayan konuşmaların parçalara ayrılmasıdır. Geliştirilen yöntemin sonunda elde edilen sessizlik içermeyen ve parçalara ayrılan konuşma bilgisi bir Türkçe Otomatik Konuşma Tanıma sistemine girdi olarak verilmiştir. Otomatik Konuşma Tanıma sisteminin çıkışında sisteme giriş olarak verilen konuşma parçalarına karşılık gelen metinler birleştirilerek sunulmuştur. Gerçekleştirilen deneylerde sessizliğin kaldırılması ve konuşmanın parçalara ayrılması işleminin Otomatik Konuşma Tanıma sistemlerinin başarımını artırdığı görülmüştür

Code-Switched Urdu ASR for Noisy Telephonic Environment using Data Centric Approach with Hybrid HMM and CNN-TDNN

Call Centers have huge amount of audio data which can be used for achieving valuable business insights and transcription of phone calls is manually tedious task. An effective Automated Speech Recognition system can accurately transcribe these calls for easy search through call history for specific context and content allowing automatic call monitoring, improving QoS through keyword search and sentiment analysis. ASR for Call Center requires more robustness as telephonic environment are generally noisy. Moreover, there are many low-resourced languages that are on verge of extinction which can be preserved with help of Automatic Speech Recognition Technology. Urdu is the $10^{th}$ most widely spoken language in the world, with 231,295,440 worldwide still remains a resource constrained language in ASR. Regional call-center conversations operate in local language, with a mix of English numbers and technical terms generally causing a "code-switching" problem. Hence, this paper describes an implementation framework of a resource efficient Automatic Speech Recognition/ Speech to Text System in a noisy call-center environment using Chain Hybrid HMM and CNN-TDNN for Code-Switched Urdu Language. Using Hybrid HMM-DNN approach allowed us to utilize the advantages of Neural Network with less labelled data. Adding CNN with TDNN has shown to work better in noisy environment due to CNN's additional frequency dimension which captures extra information from noisy speech, thus improving accuracy. We collected data from various open sources and labelled some of the unlabelled data after analysing its general context and content from Urdu language as well as from commonly used words from other languages, primarily English and were able to achieve WER of 5.2% with noisy as well as clean environment in isolated words or numbers as well as in continuous spontaneous speech.Comment: 32 pages, 19 figures, 2 tables, preprin

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

Dynamic Acoustic Unit Augmentation With BPE-Dropout for Low-Resource End-to-End Speech Recognition

With the rapid development of speech assistants, adapting server-intended automatic speech recognition (ASR) solutions to a direct device has become crucial. Researchers and industry prefer to use end-to-end ASR systems for on-device speech recognition tasks. This is because end-to-end systems can be made resource-efficient while maintaining a higher quality compared to hybrid systems. However, building end-to-end models requires a significant amount of speech data. Another challenging task associated with speech assistants is personalization, which mainly lies in handling out-of-vocabulary (OOV) words. In this work, we consider building an effective end-to-end ASR system in low-resource setups with a high OOV rate, embodied in Babel Turkish and Babel Georgian tasks. To address the aforementioned problems, we propose a method of dynamic acoustic unit augmentation based on the BPE-dropout technique. It non-deterministically tokenizes utterances to extend the token's contexts and to regularize their distribution for the model's recognition of unseen words. It also reduces the need for optimal subword vocabulary size search. The technique provides a steady improvement in regular and personalized (OOV-oriented) speech recognition tasks (at least 6% relative WER and 25% relative F-score) at no additional computational cost. Owing to the use of BPE-dropout, our monolingual Turkish Conformer established a competitive result with 22.2% character error rate (CER) and 38.9% word error rate (WER), which is close to the best published multilingual system.Comment: 16 pages, 7 figure

Low-resource speech translation

We explore the task of speech-to-text translation (ST), where speech in one language (source) is converted to text in a different one (target). Traditional ST systems go through an intermediate step where the source language speech is first converted to source language text using an automatic speech recognition (ASR) system, which is then converted to target language text using a machine translation (MT) system. However, this pipeline based approach is impractical for unwritten languages spoken by millions of people around the world, leaving them without access to free and automated translation services such as Google Translate. The lack of such translation services can have important real-world consequences. For example, in the aftermath of a disaster scenario, easily available translation services can help better co-ordinate relief efforts. How can we expand the coverage of automated ST systems to include scenarios which lack source language text? In this thesis we investigate one possible solution: we build ST systems to directly translate source language speech into target language text, thereby forgoing the dependency on source language text. To build such a system, we use only speech data paired with text translations as training data. We also specifically focus on low-resource settings, where we expect at most tens of hours of training data to be available for unwritten or endangered languages. Our work can be broadly divided into three parts. First we explore how we can leverage prior work to build ST systems. We find that neural sequence-to-sequence models are an effective and convenient method for ST, but produce poor quality translations when trained in low-resource settings. In the second part of this thesis, we explore methods to improve the translation performance of our neural ST systems which do not require labeling additional speech data in the low-resource language, a potentially tedious and expensive process. Instead we exploit labeled speech data for high-resource languages which is widely available and relatively easier to obtain. We show that pretraining a neural model with ASR data from a high-resource language, different from both the source and target ST languages, improves ST performance. In the final part of our thesis, we study whether ST systems can be used to build applications which have traditionally relied on the availability of ASR systems, such as information retrieval, clustering audio documents, or question/answering. We build proof-of-concept systems for two downstream applications: topic prediction for speech and cross-lingual keyword spotting. Our results indicate that low-resource ST systems can still outperform simple baselines for these tasks, leaving the door open for further exploratory work. This thesis provides, for the first time, an in-depth study of neural models for the task of direct ST across a range of training data settings on a realistic multi-speaker speech corpus. Our contributions include a set of open-source tools to encourage further research

Culture Clubs: Processing Speech by Deriving and Exploiting Linguistic Subcultures

Spoken language understanding systems are error-prone for several reasons, including individual speech variability. This is manifested in many ways, among which are differences in pronunciation, lexical inventory, grammar and disfluencies. There is, however, a lot of evidence pointing to stable language usage within subgroups of a language population. We call these subgroups linguistic subcultures. The two broad problems are defined and a survey of the work in this space is performed. The two broad problems are: linguistic subculture detection, commonly performed via Language Identification, Accent Identification or Dialect Identification approaches; and speech and language processing tasks taken which may see increases in performance by modeling for each linguistic subculture. The data used in the experiments are drawn from four corpora: Accents of the British Isles (ABI), Intonational Variation in English (IViE), the NIST Language Recognition Evaluation Plan (LRE15) and Switchboard. The speakers in the corpora come from different parts of the United Kingdom and the United States and were provided different stimuli. From the speech samples, two features sets are used in the experiments. A number of experiments to determine linguistic subcultures are conducted. The set of experiments cover a number of approaches including the use traditional machine learning approaches shown to be effective for similar tasks in the past, each with multiple feature sets. State-of-the-art deep learning approaches are also applied to this problem. Two large automatic speech recognition (ASR) experiments are performed against all three corpora: one, monolithic experiment for all the speakers in each corpus and another for the speakers in groups according to their identified linguistic subcultures. For the discourse markers labeled in the Switchboard corpus, there are some interesting trends when examined through the lens of the speakers in their linguistic subcultures. Two large dialogue acts experiments are performed against the labeled portion of the Switchboard corpus: one, monocultural (or monolithic ) experiment for all the speakers in each corpus and another for the speakers in groups according to their identified linguistic subcultures. We conclude by discussing applications of this work, the changing landscape of natural language processing and suggestions for future research

Multinomial logistic regression probability ratio-based feature vectors for Malay vowel recognition

Vowel Recognition is a part of automatic speech recognition (ASR) systems that classifies speech signals into groups of vowels. The performance of Malay vowel recognition (MVR) like any multiclass classification problem depends largely on Feature Vectors (FVs). FVs such as Mel-frequency Cepstral Coefficients (MFCC) have produced high error rates due to poor phoneme information. Classifier transformed probabilistic features have proved a better alternative in conveying phoneme information. However, the high dimensionality of the probabilistic features introduces additional complexity that deteriorates ASR performance. This study aims to improve MVR performance by proposing an algorithm that transforms MFCC FVs into a new set of features using Multinomial Logistic Regression (MLR) to reduce the dimensionality of the probabilistic features. This study was carried out in four phases which are pre-processing and feature extraction, best regression coefficients generation, feature transformation, and performance evaluation. The speech corpus consists of 1953 samples of five Malay vowels of /a/, /e/, /i/, /o/ and /u/ recorded from students of two public universities in Malaysia. Two sets of algorithms were developed which are DBRCs and FELT. DBRCs algorithm determines the best regression coefficients (DBRCs) to obtain the best set of regression coefficients (RCs) from the extracted 39-MFCC FVs through resampling and data swapping approach. FELT algorithm transforms 39-MFCC FVs using logistic transformation method into FELT FVs. Vowel recognition rates of FELT and 39-MFCC FVs were compared using four different classification techniques of Artificial Neural Network, MLR, Linear Discriminant Analysis, and k-Nearest Neighbour. Classification results showed that FELT FVs surpass the performance of 39-MFCC FVs in MVR. Depending on the classifiers used, the improved performance of 1.48% - 11.70% was attained by FELT over MFCC. Furthermore, FELT significantly improved the recognition accuracy of vowels /o/ and /u/ by 5.13% and 8.04% respectively. This study contributes two algorithms for determining the best set of RCs and generating FELT FVs from MFCC. The FELT FVs eliminate the need for dimensionality reduction with comparable performances. Furthermore, FELT FVs improved MVR for all the five vowels especially /o/ and /u/. The improved MVR performance will spur the development of Malay speech-based systems, especially for the Malaysian community

Language variation, automatic speech recognition and algorithmic bias

In this thesis, I situate the impacts of automatic speech recognition systems in relation to sociolinguistic theory (in particular drawing on concepts of language variation, language ideology and language policy) and contemporary debates in AI ethics (especially regarding algorithmic bias and fairness). In recent years, automatic speech recognition systems, alongside other language technologies, have been adopted by a growing number of users and have been embedded in an increasing number of algorithmic systems. This expansion into new application domains and language varieties can be understood as an expansion into new sociolinguistic contexts. In this thesis, I am interested in how automatic speech recognition tools interact with this sociolinguistic context, and how they affect speakers, speech communities and their language varieties. Focussing on commercial automatic speech recognition systems for British Englishes, I first explore the extent and consequences of performance differences of these systems for different user groups depending on their linguistic background. When situating this predictive bias within the wider sociolinguistic context, it becomes apparent that these systems reproduce and potentially entrench existing linguistic discrimination and could therefore cause direct and indirect harms to already marginalised speaker groups. To understand the benefits and potentials of automatic transcription tools, I highlight two case studies: transcribing sociolinguistic data in English and transcribing personal voice messages in isiXhosa. The central role of the sociolinguistic context in developing these tools is emphasised in this comparison. Design choices, such as the choice of training data, are particularly consequential because they interact with existing processes of language standardisation. To understand the impacts of these choices, and the role of the developers making them better, I draw on theory from language policy research and critical data studies. These conceptual frameworks are intended to help practitioners and researchers in anticipating and mitigating predictive bias and other potential harms of speech technologies. Beyond looking at individual choices, I also investigate the discourses about language variation and linguistic diversity deployed in the context of language technologies. These discourses put forward by researchers, developers and commercial providers not only have a direct effect on the wider sociolinguistic context, but they also highlight how this context (e.g., existing beliefs about language(s)) affects technology development. Finally, I explore ways of building better automatic speech recognition tools, focussing in particular on well-documented, naturalistic and diverse benchmark datasets. However, inclusive datasets are not necessarily a panacea, as they still raise important questions about the nature of linguistic data and language variation (especially in relation to identity), and may not mitigate or prevent all potential harms of automatic speech recognition systems as embedded in larger algorithmic systems and sociolinguistic contexts