Voice trigger detection from LVCSR hypothesis lattices using bidirectional lattice recurrent neural networks

双向中英被动语句语料库用于机器翻译

Bidirectional Chinese and English Passive Sentences Dataset for Machine Translation

本文提出一个双向多领域被动语句语料库，用于评估机器翻译中被动语句的处理能力，发现模型更受源文本语气影响，且LLMs在翻译多样性上表现更优。

PACT：一种用于表达性与保留语音的语音到语音翻译的实用提示框架

PACT: A Pragmatic Prompting Framework for Expressive and Voice-Preserving Speech-to-Speech Translation

针对现有语音到语音翻译系统难以保留情感等语用特征的问题，提出PACT框架，经双向西英TOPI评估，其在多维度指标上取得最优整体性能。

Streaming Punctuation: A Novel Punctuation Technique Leveraging Bidirectional Context for Continuous Speech Recognition

While speech recognition Word Error Rate (WER) has reached human parity for English, continuous speech recognition scenarios such as voice typing and meeting transcriptions still suffer from segmentation and punctuation problems, resulting from irregular pausing patterns or slow speakers. Transformer sequence tagging models are effective at capturing long bi-directional context, which is crucial for automatic punctuation. Automatic Speech Recognition (ASR) production systems, however, are constrained by real-time requirements, making it hard to incorporate the right context when making punctua…

OmniFlatten：面向无缝语音对话的端到端GPT模型

OmniFlatten: An End-to-end GPT Model for Seamless Voice Conversation

本文提出端到端GPT模型OmniFlatten，通过多阶段后训练和扁平化数据操作将文本LLM适配为全双工语音对话模型，实现低延迟自然双向对话。

Automatic speech recognition for launch control center communication using recurrent neural networks with data augmentation and custom language model

Transcribing voice communications in NASA's launch control center is important for information utilization. However, automatic speech recognition in this environment is particularly challenging due to the lack of training data, unfamiliar words in acronyms, multiple different speakers and accents, and conversational characteristics of speaking. We used bidirectional deep recurrent neural networks to train and test speech recognition performance. We showed that data augmentation and custom language models can improve speech recognition accuracy. Transcribing communications from the launch contr…

Complementary Language Model and Parallel Bi-LRNN for False Trigger Mitigation

False triggers in voice assistants are unintended invocations of the assistant, which not only degrade the user experience but may also compromise privacy. False trigger mitigation (FTM) is a process to detect the false trigger events and respond appropriately to the user. In this paper, we propose a novel solution to the FTM problem by introducing a parallel ASR decoding process with a special language model trained from "out-of-domain" data sources. Such language model is complementary to the existing language model optimized for the assistant task. A bidirectional lattice RNN (Bi-LRNN) clas…