arXiv AI
Towards Model as a Library: Offline, Community-Sourced AI for Low-Resource African Languages
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DONDO: Open w2v-BERT Speech-Recognition Base Models for African Languages
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arXiv AI
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MENASpeechBank: A Reference Voice Bank with Persona-Conditioned Multi-Turn Conversations for AudioLLMs
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Evaluating Bias in Phoneme-Based Automatic Speech Recognition Systems: An Analysis of IPA Transcription Models
The paper evaluates bias in phoneme-based automatic speech recognition (ASR) systems, focusing on WhisperIPA and ZIPA, which produce International Phonetic Alphabet (IPA) transcriptions. Using multilingual speech corpora and demographically annotated English datasets, the authors compare model-generated IPA against grapheme-to-phoneme (G2P) outputs with both standard phoneme error rate (PER) and a new Soft PER metric that allows linguistically similar substitutions. The study finds persistent disparities across language, gender, accent, ethnicity, and age, even when accounting for acceptable phonemic variation.
arXiv Computation and Language
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End-to-end Jordanian dialect speech-to-text self-supervised learning framework
arXiv:2609.24410v1 Announce Type: new Abstract: Speech-to-text engines are extremely needed nowadays for different applications, representing an essential enabler in human-robot interaction. Still, s...
arXiv AI
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Talk2Agent: Benchmarking Voice Interfaces for Text Agents
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arXiv AI
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Language Family Matters: Evaluating LLM-Based ASR Across Linguistic Boundaries
The paper introduces a new strategy for connecting large language models (LLMs) to speech encoders in automatic speech recognition (ASR) systems by sharing a single connector across languages within the same linguistic family. This approach reduces the number of parameters needed compared to training a separate connector for each language, while improving generalization across different domains and real‑world corpora. Experiments with two multilingual LLMs and two speech datasets demonstrate that family‑based connectors are both efficient and effective for multilingual ASR deployment.