Arrow Research search

Author name cluster

Tom Bagby

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
2 author rows

Possible papers

2

NeurIPS Conference 2025 Conference Paper

Massive Sound Embedding Benchmark (MSEB)

  • Georg Heigold
  • Ehsan Variani
  • Tom Bagby
  • Cyril Allauzen
  • Ji Ma
  • Shankar Kumar
  • Michael D Riley

Audio is a critical component of multimodal perception, and any truly intelligent system must demonstrate a wide range of auditory capabilities. These capabilities include transcription, classification, retrieval, reasoning, segmentation, clustering, reranking, and reconstruction. Fundamentally, each task involves transforming a raw audio signal into a meaningful 'embedding'—be it a single vector, a sequence of continuous or discrete representations, or another structured form—which then serves as the basis for generating the task's final response. To accelerate progress towards robust machine auditory intelligence, we present the Massive Sound Embedding Benchmark (MSEB): an extensible framework designed to evaluate the auditory components of any multimodal system. In its first release, MSEB offers a comprehensive suite of eight core tasks, with more planned for the future, supported by diverse datasets, including the new, large-scale Simple Voice Questions (SVQ) dataset. Our initial experiments establish clear performance headrooms, highlighting the significant opportunity to improve real-world multimodal experiences where audio is a core signal. We encourage the research community to use MSEB to assess their algorithms and contribute to its growth. The library is publicly hosted at https: //github. com/google-research/mseb.

PDF Details

ICLR Conference 2020 Conference Paper

Semi-Supervised Generative Modeling for Controllable Speech Synthesis

  • Raza Habib
  • Soroosh Mariooryad
  • Matt Shannon
  • Eric Battenberg
  • R. J. Skerry-Ryan
  • Daisy Stanton
  • David Kao
  • Tom Bagby

We present a novel generative model that combines state-of-the-art neural text- to-speech (TTS) with semi-supervised probabilistic latent variable models. By providing partial supervision to some of the latent variables, we are able to force them to take on consistent and interpretable purposes, which previously hasn’t been possible with purely unsupervised methods. We demonstrate that our model is able to reliably discover and control important but rarely labelled attributes of speech, such as affect and speaking rate, with as little as 1% (30 minutes) supervision. Even at such low supervision levels we do not observe a degradation of synthesis quality compared to a state-of-the-art baseline. We will release audio samples at https://google.github.io/tacotron/publications/semisupervised_generative_modeling_for_controllable_speech_synthesis/.

Details
v2026.09.13