Tu Anh Nguyen, Eugene Kharitonov, Jade Copet, Yossi Adi, Wei-Ning Hsu, Ali Elkahky, Paden Tomasello, Robin Algayres, Benoît Sagot, Abdelrahman Mohamed, Emmanuel Dupoux
| Challenge: | dGSLM is the first “textless” model able to generate audio samples of naturalistic spoken dialogues. |
| Approach: | They propose a model that generates speech, laughter, and other paralinguistic signals in two channels simultaneously and reproduces more naturalistic turn taking compared to a text-based cascaded model. |
| Outcome: | The proposed model reproduces more naturalistic and fluid turn taking than a text-based cascaded model. |
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Behavior-SD: Behaviorally Aware Spoken Dialogue Generation with Large Language Models (2025.naacl-long)
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| Challenge: | Spoken dialogues lack explicit modeling of behavior traits that are often overlooked in language models . et al.: our work opens new possibilities for developing behaviorally-aware dialogue systems . |
| Approach: | They propose a large-scale dataset with over 100K spoken dialogues (2,164 hours) they propose BeDLM, the first dialogue model capable of generating natural conversations . |
| Outcome: | The proposed model outperforms baseline models in generating natural dialogues . the proposed model can generate natural conversations conditioned on behavioral and narrative contexts - a key feature of spoken language models . |
Text-Free Prosody-Aware Generative Spoken Language Modeling (2022.acl-long)
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Eugene Kharitonov, Ann Lee, Adam Polyak, Yossi Adi, Jade Copet, Kushal Lakhotia, Tu Anh Nguyen, Morgane Riviere, Abdelrahman Mohamed, Emmanuel Dupoux, Wei-Ning Hsu
| Challenge: | Experimental results show that generative spoken language models (LMs) are natural unsupervised multitask learners. |
| Approach: | They propose a prosody-aware generative spoken language model that uses discovered units to generate natural, meaningful, and coherent speech. |
| Outcome: | The proposed model can generate natural, meaningful, and coherent speech given a spoken prompt. |
On Generative Spoken Language Modeling from Raw Audio (2021.tacl-1)
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Kushal Lakhotia, Eugene Kharitonov, Wei-Ning Hsu, Yossi Adi, Adam Polyak, Benjamin Bolte, Tu-Anh Nguyen, Jade Copet, Alexei Baevski, Abdelrahman Mohamed, Emmanuel Dupoux
| Challenge: | Using a set of metrics to evaluate the learned representations, we aim to create a system that learns from natural interactions as infants learn their first language. |
| Approach: | They propose a task of learning acoustic and linguistic characteristics from raw audio and a set of metrics to evaluate the learned representations at acustic, linguistic and encoding levels. |
| Outcome: | The proposed models evaluate the learned representations at acoustic and linguistic levels for both encoding and generation. |
Text-Free Image-to-Speech Synthesis Using Learned Segmental Units (2021.acl-long)
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| Challenge: | Existing models for synthesising fluent, natural-sounding spoken audio captions do not require natural language text as an intermediate representation or source of supervision. |
| Approach: | They propose a model for directly synthesizing fluent, natural-sounding spoken audio captions for images that does not require natural language text as an intermediate representation or source of supervision. |
| Outcome: | The proposed model captures diverse visual semantics of images and can replace text with a set of discrete, sub-word speech units. |
DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation (2020.acl-demos)
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Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan
| Challenge: | DIALOGPT is a large, tunable neural conversational response generation model . trained on 147M conversation-like exchanges extracted from Reddit comment chains . |
| Approach: | They present a large, tunable neural conversational response generation model, DIALOGPT . the model is trained on 147M conversation-like exchanges extracted from Reddit comment chains . |
| Outcome: | The proposed model can generate more relevant, contentful and context-consistent responses than baseline systems. |
Pretrained Language Models for Dialogue Generation with Multiple Input Sources (2020.findings-emnlp)
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| Challenge: | Large-scale pretrained language models have achieved outstanding performance on natural language understanding tasks. |
| Approach: | They propose to fuse attention information from multiple input sources to achieve better relevance with dialogue history than simple fusion baselines. |
| Outcome: | The proposed models deliver higher relevance with dialogue history than baselines. |
Diversifying Dialogue Generation with Non-Conversational Text (2020.acl-main)
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| Challenge: | Neural network-based sequence-to-sequence models suffer from low diversity in open-domain dialogue generation. |
| Approach: | They propose a way to diversify dialogue generation by leveraging non-conversational text . they collect large-scale corpus from forum comments, idioms and book snippets . |
| Outcome: | The proposed model produces significantly more diverse responses without sacrificing relevance with context. |
More Diverse Dialogue Datasets via Diversity-Informed Data Collection (2020.acl-main)
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| Challenge: | Existing approaches to generate conversational dialogue produce uninteresting, predictable responses. |
| Approach: | They propose a method to collect and determine more diverse data from conversational participants . they use dynamically computed corpus-level statistics to determine which conversational participant to collect data from . |
| Outcome: | The proposed method produces significantly more diverse data than baseline methods and better results on emotion classification and dialogue generation tasks. |
Generative Pre-trained Speech Language Model with Efficient Hierarchical Transformer (2024.acl-long)
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| Challenge: | Experimental results indicate that GPST significantly outperforms the existing speech language models in terms of word error rate, speech quality, and speaker similarity. |
| Approach: | They propose a hierarchical transformer that quantizes audio waveforms into two distinct types of discrete speech representations and integrates them within a transformer architecture. |
| Outcome: | The proposed model outperforms existing speech language models in word error rate, speech quality, and speaker similarity. |
Generative Spoken Language Model based on continuous word-sized audio tokens (2023.emnlp-main)
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| Challenge: | Text-based language models outperform character-based models, but speech inputs are 20ms or 40ms-long discrete units. |
| Approach: | They propose a generative language model based on word-size continuous audio tokens . they replace lookup table for lexical types with a Lexical Embedding function . |
| Outcome: | The proposed model is five times more memory efficient than discrete unit GSLMs and is phonetically and semantically interpretable. |