Categories: FAANG

Integrating Categorical Features in End-To-End ASR

All-neural, end-to-end ASR systems gained rapid interest from the speech recognition community. Such systems convert speech input to text units using a single trainable neural network model. E2E models require large amounts of paired speech text data that is expensive to obtain. The amount of data available varies across different languages and dialects. It is critical to make use of all these data so that both low resource languages and high resource languages can be improved. When we want to deploy an ASR system for a new application domain, the amount of domain specific training data is…

Meta returns to open source AI with Omnilingual ASR models that can transcribe 1,600+ languages natively

Meta has just released a new multilingual automatic speech recognition (ASR) system supporting 1,600+ languages — dwarfing OpenAI’s open source Whisper model, which supports just 99. Is architecture also allows developers to extend that support to thousands more. Through a feature called zero-shot in-context learning, users can provide a few…

November 11, 2025

In "AI/ML News"

Corpus Synthesis for Zero-shot ASR Domain Adaptation using Large Language Models

While Automatic Speech Recognition (ASR) systems are widely used in many real-world applications, they often do not generalize well to new domains and need to be finetuned on data from these domains. However, target-domain data is usually not readily available in many scenarios. In this paper, we propose a new…

March 15, 2024

In "FAANG"

Optimizing Byte-level Representation for End-to-End ASR

In this paper, we propose an algorithm to optimize a byte-level representation for end-to-end (E2E) automatic speech recognition (ASR). Byte-level representation is often used by large scale multilingual ASR systems when the character set of the supported languages is large. The compactness and universality of byte-level representation allow the ASR…

August 31, 2024

In "FAANG"

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