Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion (P19-1)
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| Challenge: | Existing speech recognition systems are built at individual, isolated utterance level to make building systems computationally feasible. |
| Approach: | They propose to use text-based external word and/or sentence embeddings to integrate conversational context information into a single neural network model. |
| Outcome: | The proposed model outperforms standard end-to-end speech recognition models on the Switchboard conversational speech corpus and improves word error rate with better conversational-context representation. |
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| Challenge: | Existing speech recognition models are built at a sentence level, and therefore it may not capture conversational context information. |
| Approach: | They propose a direct acoustic-to-word, end-to end speech recognition model that integrates a conversational context with other available information and directly recognizes words from speech. |
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| Challenge: | Until recently, the only feasible approach to translating acoustic speech signals into text was the cascaded approach. |
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| Challenge: | Existing approaches to connectionist temporal classification (CTC) are based on pre-trained language models (LMs) |
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Tutorial: End-to-End Speech Translation (2021.eacl-tutorials)
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| Challenge: | Speech translation is the translation of speech in one language typically to text in another, traditionally accomplished through a combination of automatic speech recognition and machine translation. |
| Approach: | This tutorial introduces the techniques used in cutting-edge research on speech translation. |
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| Challenge: | Document-level contextual information has shown benefits to text-based machine translation, but whether and how it helps end-to-end speech translation is still under-studied. |
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Spoken Conversational Agents with Large Language Models (2025.emnlp-tutorials)
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| Challenge: | This tutorial focuses on the evolution of voice-native LLMs . it reviews the adaptation of text LLM to audio, cross-modal alignment, and joint speech–text training . |
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A Purely End-to-End System for Multi-speaker Speech Recognition (P18-1)
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| Challenge: | Existing methods for multi-speaker speech recognition require isolated source signals or senone alignments for effective learning. |
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