Papers with speech recognition models
Breaking Down Power Barriers in On-Device Streaming ASR: Insights and Solutions (2025.naacl-industry)
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Yang Li, Yuan Shangguan, Yuhao Wang, Liangzhen Lai, Ernie Chang, Changsheng Zhao, Yangyang Shi, Vikas Chandra
| Challenge: | Streaming automatic speech recognition models use high power consumption to improve usability and accuracy. |
| Approach: | They propose to optimize on-device speech recognition models by adjusting component energy sensitivities based on their specific energy sensitities to reduce power consumption. |
| Outcome: | The proposed approach achieves up to 47% lower energy usage while preserving comparable model accuracy and improving real-time performance compared to leading methods. |
A Crowdsourced Open-Source Kazakh Speech Corpus and Initial Speech Recognition Baseline (2021.eacl-main)
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Yerbolat Khassanov, Saida Mussakhojayeva, Almas Mirzakhmetov, Alen Adiyev, Mukhamet Nurpeiissov, Huseyin Atakan Varol
| Challenge: | The Kazakh speech corpus contains over 153,000 utterances spoken by participants from different regions and age groups, as well as both genders. |
| Approach: | They propose to build an open-source Kazakh speech corpus for the Kazakh language that contains over 153,000 transcribed audio . they describe the data collection and preprocessing procedures followed by a description of the database specifications. |
| Outcome: | The Kazakh speech corpus contains over 153,000 utterances spoken by participants from different regions and age groups, as well as both genders. |
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. |
SD-QA: Spoken Dialectal Question Answering for the Real World (2021.findings-emnlp)
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| Challenge: | Existing QA benchmarks do not account for errors that speech recognition models might introduce . evaluating production-ready QA systems on data that is not representative of real-world inputs is problematic . |
| Approach: | They construct a multi-dialect, spoken QA benchmark on five languages with 68k audio prompts in 24 dialects from 255 speakers. |
| Outcome: | The proposed model is based on 68k audio prompts in 24 dialects from 255 speakers. |
Acoustic-to-Word Models with Conversational Context Information (N19-1)
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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. |
| Outcome: | The proposed model outperforms a standard end-to-end speech recognition system on the Switchboard conversational speech corpus and shows that it is more accurate than existing models. |
Non-parallel Accent Transfer based on Fine-grained Controllable Accent Modelling (2023.findings-emnlp)
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| Challenge: | Existing accent transfer methods rely on parallel data or speech recognition models. |
| Approach: | They propose to use mutual information learning to disentangle accent features and control the accent of the generated speech during the inference time. |
| Outcome: | The proposed framework achieves superior performance to baseline models in accentedness and audio quality. |
AlloVera: A Multilingual Allophone Database (2020.lrec-1)
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David R. Mortensen, Xinjian Li, Patrick Littell, Alexis Michaud, Shruti Rijhwani, Antonios Anastasopoulos, Alan W Black, Florian Metze, Graham Neubig
| Challenge: | Phonemes are contrastive phonological units, and allophones are their various concrete realizations. |
| Approach: | They propose a resource that maps allophones to phonemes for 14 languages . they propose phonological representations that are much closer to a universal transcription . |
| Outcome: | The proposed resource maps from 218 allophones to phonemes for 14 languages. |
Mask the Correct Tokens: An Embarrassingly Simple Approach for Error Correction (2022.emnlp-main)
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| Challenge: | Text error correction methods usually use the source (incorrect) sentence as encoder input and generate the target (correct) sentences through the decoder. |
| Approach: | They propose a method to correct errors in text sequences by randomly masking out the correct tokens in the source sentence. |
| Outcome: | The proposed method improves accuracy on Mandarin and English datasets with autoregressive and non-autoregressive generation models. |
Towards Language-Agnostic STIPA: Universal Phonetic Transcription to Support Language Documentation at Scale (2025.emnlp-main)
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| Challenge: | Existing ASR systems focus on orthographic output for high-resource languages, but STIPA can be used as a language-agnostic interface for documenting under-resourced and unwritten languages. |
| Approach: | They propose to use the International Phonetic Alphabet (STIPA) to generate phonetic transcriptions using a language-agnostic interface. |
| Outcome: | The proposed model reduces phonetic error rates even in low-resource settings and can be used for documenting under-resourced and unwritten languages. |