Google Research

Speech Processing

The research goal for speech at Google aligns with our company mission: to organize the world’s information and make it universally accessible and useful. Our pioneering research work in speech processing has enabled us to build automatic speech recognition (ASR) and text-to-speech (TTS) systems that are used across Google products, with support for more than a hundred language varieties spoken across the globe. From Gboard dictation to transcriptions of voice notes, from YouTube captions to team meetings without language barriers, and from Google Maps speaking directions aloud to Google Assistant reading the news, Google’s speech research has unparalleled reach and impact. We aim to solve speech for everyone, everywhere – and work to further improve quality, speed and versatility across all kinds of speech. We're also committed to expanding our language coverage, and have set a moonshot goal to build speech technologies for 1,000 languages.

Google's speech research efforts push the state-of-the-art on architectures and algorithms used across areas like speech recognition, text-to-speech synthesis, keyword spotting, speaker recognition, and language identification. The systems we build are deployed on servers in Google’s data centers but also increasingly on-device. The team has a passion for research that leads to product advances for the billions of users that use speech in Google products today. We also release academic publications and open-source projects for the broader research community to leverage.

Our speech technologies are embedded in products like the Assistant, Search, Gboard, Translate, Maps, YouTube, Cloud, and many more. Thanks to close collaborations with product teams, we are in a unique position to deliver user-centric research. Our researchers can conduct live experiments to test and benchmark new algorithms directly in a realistic controlled environment. Whether these are algorithmic improvements or user experience and human-computer interaction studies, we focus on solving real problems with real impact on users.

We value our user diversity, and have made it a priority to deliver the best performance to every language and language variety. Today, our speech systems operate in more than 130 language varieties, and we continue to expand our reach. The challenges of internationalizing at scale are immense and rewarding. We are breaking new ground by deploying speech technologies that help people communicate, access information online, and share their knowledge – all in their language. And combined with the unprecedented translation capabilities of Google Translate, we are also at the forefront of research in speech-to-speech translation and one step closer to a universal translator.

Recent Publications

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Dialect Bias in Speech Recognition Across 10 Spanish and French Varieties
Rodrigo Nieto
Maria Angelika-Nikita
Diane Sarkis
Diyi Yang
Proceedings of Interspeech 2026, ISCA
Preview abstract ASR bias research is largely English-focused and the mechanisms underlying performance disparities remain poorly understood. We address both gaps for Spanish and French. Using a 20-hour, gender-balanced corpus (the first for these languages designed for dialect bias analysis, with country-level stratification and manual transcripts preserving dialectal features), we benchmark seven models and find non-uniform disparities: models favor European varieties in French, while Spanish reveals unexpected patterns with Dominican outperforming Peninsular and with Chilean showing the highest error rates. Lexical analysis links failures to morphosyntactic "correction" and regional discourse markers; acoustic analysis shows that speaker embeddings encode dialect information; a diagnostic fine-tuning experiment localizes these errors to the decoder. Together, these results demonstrate that ASR errors systematically reflect linguistic distance from training distributions. View details
WAXAL: A large-scale multilingual African language speech corpus
Abdoulaye Diack
Perry Nelson
MohamedElfatih MohamedKhair
Subhashini Venugopalan
Emmanuel Asiedu Brempong
Tavonga Siyavora
Bob MacDonald
Uche Okonkwo
Sandy Ritchie
Mandy Jordan
Abhishek Bapna
Daan van Esch
Vusumuzi Dube
Jason Hickey
Ronit Levavi Morad
Yossi Matias
Jeff Dean
Aisha Walcott-Bryant
Avinatan Hassidim
2026
Preview abstract Recent advances in speech technology predominantly favor high-resource languages, leaving speakers of Sub-Saharan African languages underserved. To bridge this divide, we introduce WAXAL, an open, large-scale speech dataset spanning 24 African languages representing over 100 million speakers. WAXAL comprises over 14,100 hours of speech collected in partnership with African academic and community organizations to build local research capacity: an Automatic Speech Recognition (ASR) corpus with 2,340 hours of transcribed, image-prompted natural speech across 19 languages (plus ~11,760 untranscribed hours) and a studio-quality Text-to-Speech (TTS) corpus exceeding 235 hours. We empirically benchmark three architecturally distinct ASR models---Gemma 3n 2B, Whisper-Large-v3, and MMS-1B-all---across the 19 ASR languages on strictly speaker-disjoint splits. Fine-tuning on WAXAL yields substantial gains, reducing macro-average WER by up to 58% for Whisper-Large-v3, 56% for Gemma 3n 2B, and 17% for MMS-1B-all (and Character Error Rate by up to 70%). WAXAL-fine-tuned models generalize zero-shot to three out-of-distribution benchmarks---FLEURS, Mozilla Common Voice, and Sunbird SALT---and outperform FLEURS-trained baselines in bidirectional cross-dataset transfer. Finally, an LLM-as-judge semantic evaluation confirms that fine-tuning preserves speaker intent beyond surface-level transcription metrics. WAXAL is publicly released under a CC-BY-4.0 license at https://huggingface.co/datasets/google/WaxalNLP View details
Working Together on Technologies: A Case Study of Collaboration in Aotearoa
Ben Hutchinson
Gabi Conlon
Greg Duncum
Jamie Towler
Carrie Jones
Tane Karamaina
Hautahi Kingi
Caroline Rainsford
Ngahiwi Apanui-Barr
2026
Preview abstract This paper presents a case study in collaboration on TTS technologies between Te Taura Whiri and Google. The former is a Māori-led organisation whose mission is the revitalisation of te reo Māori. The latter is a global technology company which has a presence in Aotearoa but which is headquartered elsewhere. This case study focuses not on technical implementation details, but rather on the challenges in the partnership to mutual understanding and alignment, and the processes we co-developed to overcome those challenges, which led to the successful development of technology which was satisfactory to both partners. View details
Binamix -- A Python Library for Generating Binaural Audio Datasets
Dan Barry
Davoud Shariat Panah
Alessandro Ragano
Jan Skoglund
Andrew Hines
AES 158th Audio Engineering Society Convention (2025)
Preview abstract The increasing demand for spatial audio in applications such as virtual reality, immersive media, and spatial audio research necessitates robust solutions to generate binaural audio data sets for use in testing and validation. Binamix is an open-source Python library designed to facilitate programmatic binaural mixing using the extensive SADIE II Database, which provides Head Related Impulse Response (HRIR) and Binaural Room Impulse Response (BRIR) data for 20 subjects. The Binamix library provides a flexible and repeatable framework for creating large-scale spatial audio datasets, making it an invaluable resource for codec evaluation, audio quality metric development, and machine learning model training. A range of pre-built example scripts, utility functions, and visualization plots further streamline the process of custom pipeline creation. This paper presents an overview of the library’s capabilities, including binaural rendering, impulse response interpolation, and multi-track mixing for various speaker layouts. The tools utilize a modified Delaunay triangulation technique to achieve accurate HRIR/BRIR interpolation where desired angles are not present in the data. By supporting a wide range of parameters such as azimuth, elevation, subject Impulse Responses (IRs), speaker layouts, mixing controls, and more, the library enables researchers to create large binaural datasets for any downstream purpose. Binamix empowers researchers and developers to advance spatial audio applications with reproducible methodologies by offering an open-source solution for binaural rendering and dataset generation. We release the library under the Apache 2.0 License at https://github.com/QxLabIreland/Binamix/ View details
Project Euphonia: Advancing Inclusive Speech Recognition through Expanded Data Collection and Evaluation
Alicia Martín
Bob MacDonald
Julie Cattiau
Pan-Pan Jiang
Jimmy Tobin
Philip Q Nelson
Katrin Tomanek
Frontiers in Language Sciences (2025)
Preview abstract Speech recognition models, predominantly trained on standard speech, often exhibit lower accuracy for individuals with accents, dialects, or speech impairments. This disparity is particularly pronounced for economically or socially marginalized communities, including those with disabilities or diverse linguistic backgrounds. Project Euphonia, a Google initiative originally launched in English dedicated to improving Automatic Speech Recognition (ASR) of disordered speech, is expanding its data collection and evaluation efforts to include international languages like Spanish, Japanese, French and Hindi, in a continued effort to enhance inclusivity. This paper presents an overview of the extension of processes and methods used for English data collection to more languages and locales, progress on the collected data, and details about our model evaluation process, focusing on meaning preservation based on Generative AI. View details
Perceptual Evaluation of a Mix Presentation for Immersive Audio with IAMF
Carlos Tejeda-Ocampo
Toni Hirvonen
Ema Souza-Blanes
Mahmoud Namazi
Jan Skoglund
AES 158th Convention of the Audio Engineering Society (2025)
Preview abstract Immersive audio mix presentations involve transmitting and rendering several audio elements simultaneously. This enables next-generation applications, such as personalized playback. Using immersive loudspeaker and headphone MUSHRA tests, we investigate bitrate vs. quality for a typical mix presentation use case of a foreground stereo element, plus a background Ambisonics scene. For coding, we use Immersive Audio Model and Formats, a recently proposed system for Next-Generation Audio. Excellent quality is achieved at 384 kbit/s even with reasonable amount of personalization. We also propose a framework for content-aware analysis that can significantly reduce the bitrate when using underlying legacy audio coding instances. View details

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