A New Benchmark for Evaluating Automatic Speech Recognition in the Arabic Call Domain

• URL: http://arxiv.org/abs/2403.04280v2
• Date: Thu, 30 May 2024 12:17:51 GMT
• Title: A New Benchmark for Evaluating Automatic Speech Recognition in the Arabic Call Domain
• Authors: Qusai Abo Obaidah, Muhy Eddin Za'ter, Adnan Jaljuli, Ali Mahboub, Asma Hakouz, Bashar Al-Rfooh, Yazan Estaitia,
• Abstract summary: This work is an attempt to introduce a comprehensive benchmark for Arabic speech recognition, specifically tailored to address the challenges of telephone conversations in Arabic language. Our work aims to establish a robust benchmark that not only encompasses the broad spectrum of Arabic dialects but also emulates the real-world conditions of call-based communications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work is an attempt to introduce a comprehensive benchmark for Arabic speech recognition, specifically tailored to address the challenges of telephone conversations in Arabic language. Arabic, characterized by its rich dialectal diversity and phonetic complexity, presents a number of unique challenges for automatic speech recognition (ASR) systems. These challenges are further amplified in the domain of telephone calls, where audio quality, background noise, and conversational speech styles negatively affect recognition accuracy. Our work aims to establish a robust benchmark that not only encompasses the broad spectrum of Arabic dialects but also emulates the real-world conditions of call-based communications. By incorporating diverse dialectical expressions and accounting for the variable quality of call recordings, this benchmark seeks to provide a rigorous testing ground for the development and evaluation of ASR systems capable of navigating the complexities of Arabic speech in telephonic contexts. This work also attempts to establish a baseline performance evaluation using state-of-the-art ASR technologies.

Related papers

• The evaluation of a code-switched Sepedi-English automatic speech recognition system [0.0]
  We present the evaluation of the Sepedi-English code-switched automatic speech recognition system. This end-to-end system was developed using the Sepedi Prompted Code Switching corpus and the CTC approach. The model produced the lowest WER of 41.9%, however, the model faced challenges in recognizing the Sepedi only text.
  arXiv  Detail & Related papers  (2024-03-11T15:11:28Z)
• Improved Contextual Recognition In Automatic Speech Recognition Systems By Semantic Lattice Rescoring [4.819085609772069]
  We propose a novel approach for enhancing contextual recognition within ASR systems via semantic lattice processing. Our solution consists of using Hidden Markov Models and Gaussian Mixture Models (HMM-GMM) along with Deep Neural Networks (DNN) models for better accuracy. We demonstrate the effectiveness of our proposed framework on the LibriSpeech dataset with empirical analyses.
  arXiv  Detail & Related papers  (2023-10-14T23:16:05Z)
• End-to-End Automatic Speech Recognition model for the Sudanese Dialect [0.0]
  This paper comes to inspect the viability of designing an Automatic Speech Recognition model for the Sudanese dialect. The paper gives an overview of the Sudanese dialect and the tasks of collecting represented resources and pre-processing performed to construct a modest dataset. The designed model provided some insights into the current recognition task and reached an average Label Error Rate of 73.67%.
  arXiv  Detail & Related papers  (2022-12-21T07:35:33Z)
• Evaluation of Automated Speech Recognition Systems for Conversational Speech: A Linguistic Perspective [0.0]
  We take a linguistic perspective, and take the French language as a case study toward disambiguation of the French homophones. Our contribution aims to provide more insight into human speech transcription accuracy in conditions to reproduce those of state-of-the-art ASR systems.
  arXiv  Detail & Related papers  (2022-11-05T04:35:40Z)
• End-to-end Spoken Conversational Question Answering: Task, Dataset and Model [92.18621726802726]
  In spoken question answering, the systems are designed to answer questions from contiguous text spans within the related speech transcripts. We propose a new Spoken Conversational Question Answering task (SCQA), aiming at enabling the systems to model complex dialogue flows. Our main objective is to build the system to deal with conversational questions based on the audio recordings, and to explore the plausibility of providing more cues from different modalities with systems in information gathering.
  arXiv  Detail & Related papers  (2022-04-29T17:56:59Z)
• Discovering Phonetic Inventories with Crosslingual Automatic Speech Recognition [71.49308685090324]
  This paper investigates the influence of different factors (i.e., model architecture, phonotactic model, type of speech representation) on phone recognition in an unknown language. We find that unique sounds, similar sounds, and tone languages remain a major challenge for phonetic inventory discovery.
  arXiv  Detail & Related papers  (2022-01-26T22:12:55Z)
• Speaker-Conditioned Hierarchical Modeling for Automated Speech Scoring [60.55025339250815]
  We propose a novel deep learning technique for non-native ASS, called speaker-conditioned hierarchical modeling. We take advantage of the fact that oral proficiency tests rate multiple responses for a candidate. In our technique, we take advantage of the fact that oral proficiency tests rate multiple responses for a candidate. We extract context from these responses and feed them as additional speaker-specific context to our network to score a particular response.
  arXiv  Detail & Related papers  (2021-08-30T07:00:28Z)
• Differentiable Allophone Graphs for Language-Universal Speech Recognition [77.2981317283029]
  Building language-universal speech recognition systems entails producing phonological units of spoken sound that can be shared across languages. We present a general framework to derive phone-level supervision from only phonemic transcriptions and phone-to-phoneme mappings. We build a universal phone-based speech recognition model with interpretable probabilistic phone-to-phoneme mappings for each language.
  arXiv  Detail & Related papers  (2021-07-24T15:09:32Z)
• Accented Speech Recognition: A Survey [0.0]
  We present a survey of current promising approaches to accented speech recognition. The resulting bias in ASR performance across accents comes at a cost to both users and providers of ASR.
  arXiv  Detail & Related papers  (2021-04-21T20:21:06Z)
• Contextualized Attention-based Knowledge Transfer for Spoken Conversational Question Answering [63.72278693825945]
  Spoken conversational question answering (SCQA) requires machines to model complex dialogue flow. We propose CADNet, a novel contextualized attention-based distillation approach. We conduct extensive experiments on the Spoken-CoQA dataset and demonstrate that our approach achieves remarkable performance.
  arXiv  Detail & Related papers  (2020-10-21T15:17:18Z)
• Towards Data Distillation for End-to-end Spoken Conversational Question Answering [65.124088336738]
  We propose a new Spoken Conversational Question Answering task (SCQA) SCQA aims at enabling QA systems to model complex dialogues flow given the speech utterances and text corpora. Our main objective is to build a QA system to deal with conversational questions both in spoken and text forms.
  arXiv  Detail & Related papers  (2020-10-18T05:53:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.