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Speech large language models (LLMs) are often built using pretrained acoustic encoders as an audio interface to an LLM decoder. However, LLMs scale poorly with input sequence length, while acoustic encoders tend to produce substantially longer sequences than typical textual inputs. This motivates learning to compress e...
Recent improvements in voice-cloning speech generation systems raise concerns about misuse by malicious actors to impersonate others and spread misinformation. Detecting such tampering is difficult, since deepfakes in the wild may be created by different generative models in a wide range of languages. Furthermore, the...
Multi-user voice agents must track who said what across dialogue sessions. Text LLMs are attractive backbones for such agents, but transcripts alone do not expose acoustic speaker identity, leaving the model without a persistent reference for linking information to speakers across sessions. We address this gap by intro...
This paper presents a scalable method that measures consonant contribution using acoustic masking, and relates MMR to two linguistic factors previously reported to correlate with consonant contribution, namely phoneme frequency and functional load.
Multiparty Bench is introduced, the first benchmark specifically designed to objectively evaluate conversational speech systems as active participants within multi-party contexts and assesses agent behavior along two primary dimensions: turn-taking awareness and response appropriateness.
Experimental results show that CtrlSpeech achieves competitive zero-shot TTS performance and improves controllability over expressive attributes, demonstrating its effectiveness for flexible and practical expressive speech synthesis.
Speech large language models (SpeechLLMs) offer reduced latency and retain paralinguistic nuances that are typically lost in cascaded automatic speech recognition (ASR) and text-based LM architectures. However, they continue to lag behind text-only LLMs on complex reasoning tasks, while real-time spoken interaction imp...
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