16 citations · 31 across the 15 of their papers we have counts for
15 papers
Real-Time Voice AI Hears but Does Not Listen
Martijn Bartelds, Federico Bianchi, James Zou
Speech conveys information through both words and vocal delivery. We evaluate four leading production realtime voice systems-OpenAI's GPT Realtime 2, Google's Gemini 3.1 Flash Live…
Scaling Open Discrete Audio Foundation Models with Interleaved Semantic, Acoustic, and Text Tokens
Potsawee Manakul, Woody Haosheng Gan, Martijn Bartelds +3
Current audio language models are predominantly text-first, either extending pre-trained text LLM backbones or relying on semantic-only audio tokens, limiting general audio modelin…
"Sorry, I Didn't Catch That": How Speech Models Miss What Matters Most
Kaitlyn Zhou, Martijn Bartelds, Federico Bianchi +1
Despite speech recognition systems achieving low word error rates on standard benchmarks, they often fail on short, high-stakes utterances in real-world deployments. Here, we study…
Categorize Early, Integrate Late: Divergent Processing Strategies in Automatic Speech Recognition
Nathan Roll, Pranav Bhalerao, Martijn Bartelds +7
In speech language modeling, two architectures dominate the frontier: the Transformer and the Conformer. However, it remains unknown whether their comparable performance stems from…
False Friends Are Not Foes: Investigating Vocabulary Overlap in Multilingual Language Models
Julie Kallini, Dan Jurafsky, Christopher Potts +1
Subword tokenizers trained on multilingual corpora naturally produce overlapping tokens across languages. Does token overlap facilitate cross-lingual transfer or instead introduce…
OLMoASR: Open Models and Data for Training Robust Speech Recognition Models
Huong Ngo, Matt Deitke, Martijn Bartelds +4
Improvements in training data scale and quality have led to significant advances, yet its influence in speech recognition remains underexplored. In this paper, we present a large-s…
BLAB: Brutally Long Audio Bench
Orevaoghene Ahia, Martijn Bartelds, Kabir Ahuja +13
Developing large audio language models (LMs) capable of understanding diverse spoken interactions is essential for accommodating the multimodal nature of human communication and ca…
CTC-DRO: Robust Optimization for Reducing Language Disparities in Speech Recognition
Martijn Bartelds, Ananjan Nandi, Moussa Koulako Bala Doumbouya +3
Modern deep learning models often achieve high overall performance, but consistently fail on specific subgroups. Group distributionally robust optimization (group DRO) addresses th…
ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets
Jiatong Shi, Shih-Heng Wang, William Chen +8
ML-SUPERB evaluates self-supervised learning (SSL) models on the tasks of language identification and automatic speech recognition (ASR). This benchmark treats the models as featur…
Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation
Martijn Bartelds, Nay San, Bradley McDonnell +2
The performance of automatic speech recognition (ASR) systems has advanced substantially in recent years, particularly for languages for which a large amount of transcribed speech…
Leveraging supplementary text data to kick-start automatic speech recognition system development with limited transcriptions
Nay San, Martijn Bartelds, Blaine Billings +7
Recent research using pre-trained transformer models suggests that just 10 minutes of transcribed speech may be enough to fine-tune such a model for automatic speech recognition (A…
Quantifying Language Variation Acoustically with Few Resources
Martijn Bartelds, Martijn Wieling
Deep acoustic models represent linguistic information based on massive amounts of data. Unfortunately, for regional languages and dialects such resources are mostly not available.…
Automated speech tools for helping communities process restricted-access corpora for language revival efforts
Nay San, Martijn Bartelds, Tolúlopé Ògúnrèmí +6
Many archival recordings of speech from endangered languages remain unannotated and inaccessible to community members and language learning programs. One bottleneck is the time-int…
Adapting Monolingual Models: Data can be Scarce when Language Similarity is High
Wietse de Vries, Martijn Bartelds, Malvina Nissim +1
For many (minority) languages, the resources needed to train large models are not available. We investigate the performance of zero-shot transfer learning with as little data as po…
Leveraging pre-trained representations to improve access to untranscribed speech from endangered languages
Nay San, Martijn Bartelds, Mitchell Browne +9
Pre-trained speech representations like wav2vec 2.0 are a powerful tool for automatic speech recognition (ASR). Yet many endangered languages lack sufficient data for pre-training…