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Found 10 papers, 2 papers with code
Deferred NAM: Low-latency Top-K Context Injection via DeferredContext Encoding for Non-Streaming ASR
Contextual biasing enables speech recognizers to transcribe important phrases in the speaker's context, such as contact names, even if they are rare in, or absent from, the training data.
High-precision Voice Search Query Correction via Retrievable Speech-text Embedings
In this paper, we eliminate the hypothesis-audio mismatch problem by querying the correction database directly using embeddings derived from the utterance audio; the embeddings of the utterance audio and candidate corrections are produced by multimodal speech-text embedding networks trained to place the embedding of the audio of an utterance and the embedding of its corresponding textual transcript close together.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
SLM: Bridge the thin gap between speech and text foundation models
We present a joint Speech and Language Model (SLM), a multitask, multilingual, and dual-modal model that takes advantage of pretrained foundational speech and language models.
Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm
Contextual biasing refers to the problem of biasing the automatic speech recognition (ASR) systems towards rare entities that are relevant to the specific user or application scenarios.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Deliberation-based Joint Acoustic and Text Decoder
We propose a new two-pass E2E speech recognition model that improves ASR performance by training on a combination of paired data and unpaired text data.
Streaming Intended Query Detection using E2E Modeling for Continued Conversation
In voice-enabled applications, a predetermined hotword isusually used to activate a device in order to attend to the query. However, speaking queries followed by a hotword each timeintroduces a cognitive burden in continued conversations.
Speech Recognition with Augmented Synthesized Speech
Recent success of the Tacotron speech synthesis architecture and its variants in producing natural sounding multi-speaker synthesized speech has raised the exciting possibility of replacing expensive, manually transcribed, domain-specific, human speech that is used to train speech recognizers.
Improving Performance of End-to-End ASR on Numeric Sequences
This out-of-vocabulary (OOV) issue is addressed in conventional ASR systems by training part of the model on spoken domain utterances (e. g.
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.
VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking
In this paper, we present a novel system that separates the voice of a target speaker from multi-speaker signals, by making use of a reference signal from the target speaker.
