Tootfinder

Improving Code Switching with Supervised Fine Tuning and GELU Adapters
Linh Pham
https://arxiv.org/abs/2506.00291 https://arxiv.org/p…

Improving Code Switching with Supervised Fine Tuning and GELU Adapters
There are few code switching datasets, labeled or unlabled, that exist today. As a result, ASR requires new methods to utilize the vast monolingual data and models that exist. This paper uses OpenAI's open source ASR model, Whisper, which has been pre-trained on 680K hours of audio to perform monolingual ASR tasks. In Part 1, this paper examines how exploiting Whisper's monolingual ability to individually tokenize training text, called "Switching Tokenizers Method", improves transcription accur…

"As a political force, the Labour left is finished. The tactic of entryism – entering the Labour party and changing it – is finished too."
"..a string of local council victories next year would pave the way for an unprecedented challenge to #Labour – not from the right, as the party has always had to fight, but from its radical flank. And this new movement could take parts of Lab…

The British left is coming for the Government
Whisper it as yet, but after five long years of confusion and disarray, the British left is rallying. Local political organisations are coalescing, from Chiswick to Liverpool to Newcastle. The Green P

This https://arxiv.org/abs/2501.16344 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

WhiSPA: Semantically and Psychologically Aligned Whisper with Self-Supervised Contrastive and Student-Teacher Learning
Current speech encoding pipelines often rely on an additional text-based LM to get robust representations of human communication, even though SotA speech-to-text models often have a LM within. This work proposes an approach to improve the LM within an audio model such that the subsequent text-LM is unnecessary. We introduce WhiSPA (Whisper with Semantic and Psychological Alignment), which leverages a novel audio training objective: contrastive loss with a language model embedding as a teacher. …

Series C, Episode 07 - Children of Auron
C.A. TWO: Next, the young adults. Thank you, module six.
C.A. ONE: Any response to the distress calls?
C.A. TWO: Not a whisper. Appeals out on all channels, nothing.
VOICE: Medical section reporting.
https://blake.torpidity.net/m/307/119

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

Attention Is Not Always the Answer: Optimizing Voice Activity Detection with Simple Feature Fusion
Kumud Tripathi, Chowdam Venkata Kumar, Pankaj Wasnik
https://arxiv.org/abs/2506.01365

Attention Is Not Always the Answer: Optimizing Voice Activity Detection with Simple Feature Fusion
Voice Activity Detection (VAD) plays a key role in speech processing, often utilizing hand-crafted or neural features. This study examines the effectiveness of Mel-Frequency Cepstral Coefficients (MFCCs) and pre-trained model (PTM) features, including wav2vec 2.0, HuBERT, WavLM, UniSpeech, MMS, and Whisper. We propose FusionVAD, a unified framework that combines both feature types using three fusion strategies: concatenation, addition, and cross-attention (CA). Experimental results reveal that …

Series C, Episode 07 - Children of Auron
C.A. TWO: Next, the young adults. Thank you, module six.
C.A. ONE: Any response to the distress calls?
C.A. TWO: Not a whisper. Appeals out on all channels, nothing.
VOICE: Medical section reporting.
https://blake.torpidity.net/m/307/119

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

Unsupervised Rhythm and Voice Conversion to Improve ASR on Dysarthric Speech
Karl El Hajal, Enno Hermann, Sevada Hovsepyan, Mathew Magimai. -Doss
https://arxiv.org/abs/2506.01618 …

Unsupervised Rhythm and Voice Conversion to Improve ASR on Dysarthric Speech
Automatic speech recognition (ASR) systems struggle with dysarthric speech due to high inter-speaker variability and slow speaking rates. To address this, we explore dysarthric-to-healthy speech conversion for improved ASR performance. Our approach extends the Rhythm and Voice (RnV) conversion framework by introducing a syllable-based rhythm modeling method suited for dysarthric speech. We assess its impact on ASR by training LF-MMI models and fine-tuning Whisper on converted speech. Experiment…

This https://arxiv.org/abs/2505.21527 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

VietASR: Achieving Industry-level Vietnamese ASR with 50-hour labeled data and Large-Scale Speech Pretraining
Automatic speech recognition (ASR) has made remarkable progress but heavily relies on large-scale labeled data, which is scarce for low-resource languages like Vietnamese. While existing systems such as Whisper, USM, and MMS achieve promising performance, their efficacy remains inadequate in terms of training costs, latency, and accessibility. To address these issues, we propose VietASR, a novel ASR training pipeline that leverages vast amounts of unlabeled data and a small set of labeled data.…