Multimodal Approach to Device-Directed Speech Detection with Large Language Models

แนวคิดหลัก

Exploring a multimodal approach for device-directed speech detection using large language models.

บทคัดย่อ

The study explores dropping trigger phrases in virtual assistant commands.
Three approaches are tested: acoustic information, ASR decoder outputs, and a multimodal system.
Multimodal system shows significant improvements over text-only and audio-only models.
Training data includes directed and non-directed utterances with trigger phrases.
Evaluation data consists of device-directed and non-directed examples.
Feature extraction involves text, ASR features, and acoustic features.
Method involves an audio encoder, mapping networks, and a decoder-only LLM.
Large language models like GPT2 are used for text comprehension tasks.
Unimodal baselines include linear classifiers on Whisper and CLAP models.
Experiments show the effectiveness of combining text, audio, and decoder signals in a multimodal system.

สถิติ

Using multimodal information yields relative equal-error-rate improvements over text-only and audio-only models of up to 39% and 61%.
Increasing the size of the LLM leads to further relative EER reductions of up to 18% on the dataset.

คำพูด

"Large language models have demonstrated state-of-the-art text comprehension abilities."
"Multimodal information consisting of acoustic features obtained from a pretrained audio encoder yields significant improvements."

ข้อมูลเชิงลึกที่สำคัญจาก

A Multimodal Approach to Device-Directed Speech Detection with Large Language Models

by Dominik Wage... ที่ arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14438.pdf

สอบถามเพิ่มเติม

How can this multimodal approach be applied to other speech recognition tasks

This multimodal approach to device-directed speech detection can be applied to various other speech recognition tasks by leveraging the combination of acoustic, lexical, and decoder signals. For instance, in the field of automated transcription services, integrating audio features with textual information from ASR systems could enhance accuracy and efficiency. Similarly, in voice-controlled smart home devices or automotive assistants, incorporating multiple modalities can improve user interaction and command understanding. By adapting this approach to different contexts and domains, such as medical dictation or customer service call centers, the system's robustness and adaptability can be further explored.

What are the potential drawbacks or limitations of relying solely on large language models for device-directed speech detection

While large language models (LLMs) offer advanced text comprehension capabilities for device-directed speech detection, there are potential drawbacks and limitations to consider. One limitation is the computational resources required for training and fine-tuning these models on diverse multimodal data sets. Additionally, LLMs may struggle with handling noisy environments or overlapping speech scenarios where acoustic information alone might not suffice. Moreover, relying solely on LLMs may lead to biases in decision-making based on the training data provided or lack of interpretability in complex interactions between modalities.

How might advancements in this field impact the development of future virtual assistant technologies

Advancements in device-directed speech detection using multimodal approaches have significant implications for future virtual assistant technologies. These advancements could lead to more seamless interactions between users and virtual assistants without explicit trigger phrases or cues at every turn-taking point. The ability to combine acoustic features with lexical context enhances natural language understanding within a broader range of applications beyond traditional wake-word detection systems.
Furthermore, improved accuracy in distinguishing directed utterances from background noise paves the way for enhanced privacy controls where virtual assistants respond only when specifically addressed by users. This development could also facilitate personalized user experiences tailored to individual preferences through better contextual awareness derived from combined modalities like audio representations and ASR decoder signals.