താരതമ്യപഠനസംഘം ഒക്ടോബർ 1, 2 തീയതികളിലായി സംഘടിപ്പിച്ച താപസം സെമിനാർ ശ്രീശങ്കരാചാര്യ സംസ്കൃതസർവ്വകലാശാലയിൽ വെച്ച് നടന്നു. ഈ സെമിനാറിൽ ‘യൂണിക്കോഡിലെത്തിയ മലയാളം: ചില ഭാഷാസാംസ്കാരികചിചാരങ്ങൾ’ എന്ന വിഷത്തിൽ ഞാനവതരിപ്പിച്ച പ്രഭാഷണം ഇവിടെ കൊടുക്കുന്നു.
Wav2Vec2-BERT+LM: Transcribing Speech and Evaluating Models using Huggingface Transformers
What is Wav2Vec2-BERT? Wav2Vec2-BERT is a successor of the popular Wav2Vec2 Model, a pre-trained model for Automatic Speech Recognition (ASR). Wav2Vec2-BERT is a 580M-parameters audio model that has been pre-trained on 4.5M hours of unlabeled audio data covering more than 143 languages. Following the basic architecture of Wav2Vec2, with increased pretraining data and slighly different training objectives, various models (XLSR, XLS-R and MMS) with pretrained checkpoints were released.
Wav2Vec2-BERT pretrained model was introduced in the SeamlessM4T Paper by Meta in August 2023.
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Indian Languages and Text Normalization: Part 1
This is a two part article. The first part will cover how the normalization routine in the popular ASR engine Whisper, removes essential characters like vowel signs in Indian languages while evaluating the performance. The second part (yet to be written) will cover various existing libraries and the approaches needed to perform proper normalization in Indian languages. Text Normalization Text Normalization in natural language processing (NLP) refers to the conversion of different written forms of text to one standardised form.
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Call me Dr. Kavya 🤩
I was awarded doctoral degree by APJ Abdul Kalam Technological University, Kerala, India.
You can read my thesis ‘Linguistic challenges in Malayalam speech recognition: Analysis and solutions’ here.
Pics from the graduation ceremony hosted by College of Engineering Trivandrum and APJ Abdul Kalam Technological University.
Live Dictation: Malayalam speech to text using subword tokens
The research carried out as part of my PhD was centred around the linguistic challenges in Malayalam speech recognition. One of the biggest chellenges associated with recognizing speech in morphologically complex languages is centred around how granular should be the text tokens.
Classical ASR with Word tokens In the classical architecture of Automatic Speech Recognition (ASR) with word tokens, the acoustic model identifies fundamental sound units, the pronunciation lexicon maps sounds to words, and the language model predicts word sequences to convert speech to text.
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An Open Framework to Build Malayalam Speech to Text System
It was indeed a pleasure to present my paper on An openframework to develop Malayalam Speech to text Systems at the 35th Kerala Science congress held during 10th-14th of February, 2023 at Kuttikkanam, Kerala India. The work was presented in the category of Scientific Social Responsibility and recieved the best oral presentation award in that category.
The presentation was all about how I ensured openness and transperancy in the development process of speech recognition system for Malayalam done as part of my PhD work Linguistic Challenges in Malayalam Speech Recognition: Analysis and Solutions.
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How to create a Malayalam Pronuciation Dictionary?
What is a phonetic lexicon? A pronunciation dictionary or a phonetic lexicon is a list of words and their pronunciation described as a sequence of phonemes. It is an essential component in the training and decoding of speech to text (STT) and text to speech (TTS) systems. A pronunciation dictionary is slightly different from a simple phonetic transcription. It should contain delimiters between phonemes, space is usually the default choice.
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Mlphon: A Multifunctional Grapheme-Phoneme Conversion Tool Using Finite State Transducers
The Mlphon tool I had been working on, for the past couple of years was extensively expanded as part of my research work at College of Engineering Trivandrum. A detailed presentation of the phonemic features of Malayalam, their incorporation as a sequential ruleset in the form of finite state transucers, a quantitative evaluation of the applications of the tool are now available in the article, published by the open access journal, IEEE Access.
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മനുഷ്യഭാഷ, യന്ത്രബുദ്ധി
ശാസ്ത്രഗതി (ജൂൺ 2022) പ്രസിദ്ധീകരിച്ച ലേഖനം
ആമുഖം മനുഷ്യർക്കു നൈസർഗ്ഗികമായുള ഒരു കഴിവാണ് ഭാഷ. കുഞ്ഞുങ്ങൾ ഏതു ഭാഷയും അവരുടെ പരിസരങ്ങളിൽ നിന്നും സ്വാഭാവികമായി നേടിയെടുക്കുന്നു. ഈ ശേഷി ഒരു കമ്പ്യൂട്ടറിന് കൈവരിക്കാൻ അത്ര എളുപ്പമല്ല. സിനിമാടിക്കറ്റ് ബുക്ക് ചെയ്യാനും, ഭക്ഷണം ഓർഡർ ചെയ്യാനും, മെയിലയക്കാനും, അലാറം വെയ്ക്കാനുമൊക്കെ ഇംഗ്ലീഷ് ഭാഷയിൽ പറഞ്ഞാൽ ചെയ്യാൻ കഴിയുന്ന ഡിജിറ്റൽ അസിസ്റ്റന്റുകളൊക്കെ ഇന്നുണ്ട്. ഇതിനർത്ഥം യന്ത്രങ്ങൾ ഭാഷാശേഷി കൈവരിച്ചുവെന്നാണോ? മലയാളമുൾപ്പെടെയുള്ള മറ്റു ഭാഷകളും കമ്പ്യൂട്ടറുകൾക്കു വഴങ്ങുമോ? അതിനു കൃത്രിമബുദ്ധി ആവശ്യമുണ്ടോ? ഈ വിഷയങ്ങളൊക്കെ പരിശോധിക്കുകയാണ് ഈ ലേഖനത്തിൽ.
യന്ത്രങ്ങൾക്ക് സ്വയം പഠിക്കാനാകുമോ? ചുറ്റുപാടുമുള്ള ശബ്ദങ്ങൾ പിടിച്ചെടുക്കാനുള്ള ഉപകരണം എല്ലാ ഫോണുകളിലുമുണ്ട്. ആ ശബ്ദത്തിൽ നിന്നും സംസാരം വേർതിരിച്ച്, പറഞ്ഞതെന്തെന്ന് തിരിച്ചറിയാനുള്ള സംവിധാനം പല ഭാഷകളിലും ഇന്ന് സാധ്യമാണ്.
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Mozhi Malayalam TTS powered by Mlphon and Mlmorph
Krishna Sankar recently developed a Malayalam - English bilingual Text to Speech System, Mozhi. Check the web demo page and play around with your choice of words and listen to the natural speech it produces. Krishna has set a future goal of understanding the emotional content of the text and read it out accordingly. This is expected to make the application suitable for audio books. Generating audio for arbitrary speaker with very few training samples is another area he plans to work on.
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