Overview of the Fifth Shared Task on Speech Recognition for Vulnerable Individuals in T amil (2025)

Abstract: In this paper, an overview of the shared task on speech recognition for vulnerable individuals in Tamil (LT-EDI@LDK2025) is described. The work comes with a Tamil dataset that was collected from elderly individuals who identify as male, female, or transgender. The audio samples were taken in public places such as markets, vegetable shops, hospitals, etc. The training phase and the testing phase are when the dataset is made available. The task required of the participants was to handle audio signals using various models and techniques and then turn in their results as transcriptions of the provided test samples. The participant’s results were assessed using WER (Word Error Rate). The transformer-based approach was used by participants to achieve automatic voice recognition. This overview paper discusses the findings and various pre-trained transformer-based models that the participants employed.

## introduction the earliest known examples of old tamil writing are tiny inscriptions found in adichanallur that date between 905 and 696 bc. of all the indian languages, tamil possesses the most ancient non-sanskritic literature. the grammar of tamil is agglutinative, meaning that noun class, number, case, verb tense, and other grammatical categories are indicated by suffixes. unlike other aryan languages, which use sanskrit as their standard language, tamil uses tamil for both its scholarly vocabulary and its metalinguistic terminology. together with dialects, tamil has multiple forms: cankattami, the classical literary style based on the ancient language; centami, the modern literary and formal style; and kotuntami, the present vernacular form. @xcite . there is a stylistic continuity created by these styles merging together. for instance, one may write centami using cankattami vocabulary, or one could speak kotuntami while using forms related to one of the other types. @xcite . a lexical root plus one or more affixes combine to form tamil words. suffixes make up the bulk of affixes in tamil. tamil suffixes fall into two groups: derivational suffixes, which change a word's meaning or part of speech, and inflectional suffixes, which identify certain categories like person, number, mood, tense, and so on. agglutination can lead to huge words with multiple suffixes, needing numerous words or a phrase in english. its length and scope are infinite. although smart technologies have come a long way, humanmachine interaction is still being developed and enhanced. @xcite . automatic speech recognition (asr) is one such recent technology that has enabled voice-based user interfaces for numerous automated systems. many elderly and transgender people are frequently unaware of the technology @xcite that is made available to help people in public places like banks, hospitals, and administrative offices. thus, communication is the only kind of media that can assist people in getting what they want. however these asr systems are infrequently used by the elderly, transsexuals, and others with lower levels of education. english-language voice-based interfaces are a feature of most automated systems currently in use. elderly people and those living in rural areas prefer to speak in their native tongue. the provision of speech interfaces in the local language for help systems designed for public usage would be advantageous to all. information regarding spontaneous speech in tamil is gathered from transgender and elderly people who are not able to use these programs. the aim of this challenge is to find an efficient asr model to handle the elderly person's speech corpus. the representation of how the audio samples are collected is shown in fig :1 the pertinent features will first be extracted from the speech signal using an asr system. acoustic models will also be produced using these features that were retrieved. ultimately, the language model assists in converting these probabilities into grammatical words. the language model uses statistics from training data to assign probabilities to words and phrases @xcite . it is necessary to evaluate asr systems' performance prior to deploying them in real-time applications. on largescale automatic speech recognition (asr) tasks, an end-to-end speech recognition system has shown promising performance, matching or surpassing that of traditional hybrid systems. using an acoustic model, lexicon, and language model, the endto-end system quickly transforms audio data into tag labels @xcite . in the field of end-to-end voice recognition, there exist two extensively utilized frameworks. frame synchronous prediction separates one input frame from the other by giving each one a target label @xcite . phoneme identification can also be used to assess the efficacy using different test feature vectors and model settings. the use of acoustic models for speech recognition, which are created using the sounds of younger people, may have a substantial impact on the capacity to recognize elder speech @xcite . there aren't many acoustic models that can handle the voice detection task. among the acoustic models are japanese newspaper article sentences (jnas), japanese newspaper article sentences read speech corpus of the aged (s-jnas), and corpus of spontaneous japanes (csj). the csj model only achieves the lowest wer once the older voices are adjusted, according to a comparison of all the acoustic models in the literature @xcite . dialect adaptation is also required in order to improve recognition accuracy @xcite . recent advances in large vocabulary continuous speech recognition (lvcsr) technologies have led to the widespread use of speech recognition systems in several fields @xcite . variations in the acoustics of individual speakers are thought to be one of the primary causes of the decline in speech recognition rates. for elder speakers to use speech recognition systems trained on typical adult speech data, the acoustic discrepancies between their speech and that of an adult should be investigated and correctly adjusted. rather, this loss can be mitigated by an acoustic model enhanced by senior speakers' utterances, as shown by a document retrieval system. modern voice recognition technology can reach excellent recognition accuracy while speaking while reading a written text or something comparable; nevertheless, the accuracy decreases when speaking spontaneously and freely. the main reason for this issue is that the linguistic and acoustic models used in voice recognition were mostly developed using read aloud or written language materials. however, there are significant linguistic and auditory differences between written language and spontaneous speech @xcite . currently, it is becoming more and more popular to create asr systems that can detect voice data from older persons. the aging population in modern society and the proliferation of smart devices, which make information freely accessible to both the young and the old, have led to a demand for improved voice recognition in smart devices @xcite @xcite . because of the influences of speech articulation and speaking style, speech recognition systems are often optimized for the voice of an average adult and have a lower accuracy rate when recognising the voice of an elderly person. it will surely become more expensive to adapt the current voice recognition systems to handle the speech of elderly users @xcite . ## related work when a model is fine-tuned on many languages at the same time, a single multilingual speech recognition model can be built that can compete with models that are fine-tuned on individual language speech corpus. speech2vec expands the text-based word2vec model to learn word embeddings directly from speech by combining an rnn encoder-decoder framework with skipgrams or cbow for training. acoustic models are designed at phoneme/syllable level to carry out the speech recognition task. initially, the acoustic models were created with jnas, s-jnas and csj speech corpus @xcite . later, the models were trained/fine-tuned with different speech corpus. to get a better performance and accuracy, backpropagation using the transfer learning was attempted in the literature. similar work was performed for other languages like bengali, japanese, etc. also, more speech corpus is collected from the young people for many languages @xcite . however, speaker fluctuation, environmental noise, and transmission channel noise all degrade asr performance. as the shared task is given with a separate training data set, an effective model has to be created during the training. therefore, hierarchical transformer based model for large context end to end asr can be used @xcite . in the recent era, the environment is changing with smart systems and is identified that there is a need for asr systems that are capable of handling speech of elderly people spoken in their native languages. to overcome this problem, the shared task is proposed for the research commu-nity to build an efficient model for recognizing the speech of elderly people and transgenders in tamil language. findings of the automatic speech recognition for vulnerable individuals are given in @xcite ("s and b, "2023") @xcite , have used transformer models used for transformer based asr for vulnerable individuals in tamil. ## data-set description the dataset given to this shared task @xcite is an tamil conversational speech recorded from the elderly people whose average age is around 61 for male, 59 for female and 30 for transgender people. a total of 7.5 hours is collected from the elderly people. 46 audio files were recorded and each audio file is split into many subsets as transformer model does not support the large audio files. the speech is recorded with a sampling rate of 16khz. the audio files from audio -1 to audio -36 are used for training (duration is approximately 5.5 hours) and audio -37 to audio -51 are used for testing (duration is approximately 2 hours). ## methodology the methodology used by the participants in shared task of speech recognition for vulnerable individuals in tamil is discussed in this section. ## evaluation of results the results submitted by the participants are evaluated based on the wer computed between the asr hypotheses submitted by the participants and the ground truth of human speech transcription. wer ( word error rate) = ( s + d + i) / n where, @xmath0. of substitutions @xmath1. of deletions @xmath2. of insertions @xmath3. of words in the reference transcription as discussed in the methodology, different average word error rate are measured using various pre-trained transformer based models. ## conclusions the shared challenge for vulnerable voice recognition in tamil is covered in this overview paper. the speech corpus shared for this job was recorded from elderly persons. getting older people's speech more accurately recognised is a difficult endeavour. in order to boost the accuracy and performance in recognising the elderly people's speech, the participants have been given access to the gathered