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Found 13 papers, 3 papers with code
SpiRit-LM: Interleaved Spoken and Written Language Model
We introduce SPIRIT-LM, a foundation multimodal language model that freely mixes text and speech.
XLS-R fine-tuning on noisy word boundaries for unsupervised speech segmentation into words
Due to the absence of explicit word boundaries in the speech stream, the task of segmenting spoken sentences into word units without text supervision is particularly challenging.
Generative Spoken Language Model based on continuous word-sized audio tokens
In NLP, text language models based on words or subwords are known to outperform their character-based counterparts.
Big model only for hard audios: Sample dependent Whisper model selection for efficient inferences
Recent progress in Automatic Speech Recognition (ASR) has been coupled with a substantial increase in the model sizes, which may now contain billions of parameters, leading to slow inferences even with adapted hardware.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Fine-tuning Strategies for Faster Inference using Speech Self-Supervised Models: A Comparative Study
Self-supervised learning (SSL) has allowed substantial progress in Automatic Speech Recognition (ASR) performance in low-resource settings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Are word boundaries useful for unsupervised language learning?
However, word boundary information may be absent or unreliable in the case of speech input (word boundaries are not marked explicitly in the speech stream).
STOP: A dataset for Spoken Task Oriented Semantic Parsing
Furthermore, in addition to the human-recorded audio, we are releasing a TTS-generated version to benchmark the performance for low-resource domain adaptation of end-to-end SLU systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
DP-Parse: Finding Word Boundaries from Raw Speech with an Instance Lexicon
Finding word boundaries in continuous speech is challenging as there is little or no equivalent of a 'space' delimiter between words.
Speech Sequence Embeddings using Nearest Neighbors Contrastive Learning
We introduce a simple neural encoder architecture that can be trained using an unsupervised contrastive learning objective which gets its positive samples from data-augmented k-Nearest Neighbors search.
Generative Spoken Dialogue Language Modeling
We introduce dGSLM, the first "textless" model able to generate audio samples of naturalistic spoken dialogues.
The Zero Resource Speech Challenge 2020: Discovering discrete subword and word units
We present the Zero Resource Speech Challenge 2020, which aims at learning speech representations from raw audio signals without any labels.
Evaluating the reliability of acoustic speech embeddings
However, there is currently no clear methodology to compare or optimise the quality of these embeddings in a task-neutral way.
The Zero Resource Speech Challenge 2019: TTS without T
We present the Zero Resource Speech Challenge 2019, which proposes to build a speech synthesizer without any text or phonetic labels: hence, TTS without T (text-to-speech without text).
