machine learning,

Speech LLMs for Conversations

Shangeth Rajaa, Abhinav Tushar May 09, 2024 · 2 mins read

With LLMs making conversational systems has become easier. You no longer need to focus on the low-level details of categorizing semantics and designing responses. Instead, you can concentrate on controlling high-level behaviors via an LLM. This is the trend that we see most of the world moving towards as products are using vendor combinations of ASR, LLM, and TTS with some dialog management stitched in between. While this is going to be the norm soon, we want to keep exploring areas from where the next set of quality improvements will come.

Earlier we discussed how spoken conversations are richer than pure text and how the gap would be not bridged by LLMs purely working on transcriptions. In one of our recent experiments we built an efficient multi-modal LLM that takes speech directly to provide better conversational experience. For production usage, the constraint here is that this should happen without losing the flexibility that you get in a text-only LLM around writing prompts, making changes, evaluating, and debugging.

Below is a conversation with our recent in-house Speech LLM based conversational system. Notice that because of the extra information in speech some micro personalizations can happen like usage of gendered pronouns1. You also get lower impact of transcription errors and in general better responses in non-speech signals. With access to both speech and text domains, the model allows for more fluent turn-taking, though not demonstrated in the current conversation. In addition, our approach also reduces the combined model size (<2B) for taking speech to response, leading to lower compute latency as compared to larger systems.

The model above doesn’t yet control speech synthesis beyond the textual markers it can generate, but that’s something to be added soon (you might have noticed erratic pitch shifts in the call above since TTS vendors don’t contextualize based on past conversations). Stay tuned for more details on how we take this and similar research areas forward.

  1. Of course concerns around paralinguistic prediction accuracies are extremely important to take something like this in production.