# Acoustic-to-Word Models with Conversational Context Information

## TL;DR

This paper introduces an end-to-end acoustic-to-word speech recognition model that incorporates conversational context to improve recognition accuracy in long conversations, demonstrating superior performance on the Switchboard corpus.

## Contribution

It presents a novel acoustic-to-word model that effectively integrates conversational context, enhancing recognition of extended dialogues in speech recognition systems.

## Key findings

- Outperforms standard end-to-end speech recognition models
- Effective utilization of conversational context improves long conversation recognition
- Achieves better accuracy on Switchboard corpus

## Abstract

Conversational context information, higher-level knowledge that spans across sentences, can help to recognize a long conversation. However, existing speech recognition models are typically built at a sentence level, and thus it may not capture important conversational context information. The recent progress in end-to-end speech recognition enables integrating context with other available information (e.g., acoustic, linguistic resources) and directly recognizing words from speech. In this work, we present a direct acoustic-to-word, end-to-end speech recognition model capable of utilizing the conversational context to better process long conversations. We evaluate our proposed approach on the Switchboard conversational speech corpus and show that our system outperforms a standard end-to-end speech recognition system.