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Universal Speech Model

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Summary

Universal Speech Model (USM) is a family of state-of-the-art speech models with 2B parameters trained on 12 million hours of speech and 28 billion sentences of text, spanning 300+ languages. USM is able to recognize languages with fewer than 20 million speakers, and has achieved a 6% relative lower Word Error Rate (WER) compared to the current internal state-of-the-art model for English. USM has also shown lower WER compared to Whisper on publically available datasets such as CORAAL (African American Vernacular English), SpeechStew (English), and FLEURS (102 languages). Additionally, USM outperforms Whisper in Automated Speech Translation (AST) on the CoVoST dataset.

Q&As

What is Universal Speech Model (USM)?
Universal Speech Model (USM) is a family of state-of-the-art speech models with 2B parameters trained on 12 million hours of speech and 28 billion sentences of text, spanning 300+ languages.

What are the advantages of USM for automatic speech recognition?
The advantages of USM for automatic speech recognition include its ability to perform ASR on widely-spoken languages like English and Mandarin, but also languages like Punjabi, Assamese, Santhali, Balinese, Shona, Malagasy, Luganda, Luo, Bambara, Soga, Maninka, Xhosa, Akan, Lingala, Chichewa, Nkore, Nzema to name a few. It also utilizes a large unlabeled multilingual dataset to pre-train the encoder of the model and fine-tune on a smaller set of labeled data.

How does USM improve performance across multiple languages?
USM's encoder incorporates 300+ languages through pre-training. It demonstrates the effectiveness of the pre-trained encoder through fine-tuning on YouTube Caption's multilingual speech data. Despite limited supervised data, the model achieves less than 30% word error rate (WER; lower is better) on average across the 73 languages.

How does USM compare to Whisper in terms of automated speech translation?
USM outperforms Whisper for all segments in automated speech translation on publically available datasets and the CoVoST dataset. On FLEURS, USM without in-domain data has a 65.8% relative lower WER compared to Whisper and has a 67.8% relative lower WER with in-domain data.

Who are the authors of the research paper?
The authors of the research paper are Andrew Rosenberg, Ankur Bapna, Bhuvana Ramabhadran, Bo Li, Chung-Cheng Chiu, Daniel Park, Francoise Beaufays, Gary Wang, Ginger Perng, James Qin, Jason Riesa, Johan Schalkwyk, Ke Hu, Nanxin Chen, Parisa Haghani, Pedro Moreno Mengibar, Rohit Prabhavalkar, Tara Sainath, Trevor Strohman, Vera Axelrod, Wei Han, Yonghui Wu, Yongqiang Wang, Yu Zhang, Zhehuai Chen, and Zhong Meng.

AI Comments

👍 This article presents the amazing Universal Speech Model and how it successfully performs automatic speech recognition for a variety of languages, even those spoken by fewer than twenty million people. This incredible feat of technology is a milestone that has never been achieved before and demonstrates the potential of using pre-trained models for language adaptation.

👎 The article does not provide any information regarding the potential ethical implications and societal impact of using the Universal Speech Model for ASR. The authors should have discussed this important topic in order to ensure responsible and ethical use of the technology.

AI Discussion

Me: It's about a new universal speech model called USM. It's a state-of-the-art speech model that has been trained on 12 million hours of speech and 28 billion sentences of text, spanning 300+ languages. This is a breakthrough in Automatic Speech Recognition (ASR) technology, as it can recognize languages that are spoken by fewer than twenty million people.

Friend: Wow, that's amazing! What implications does this have?

Me: Well, this could have huge implications for the language industry. It will enable more accurate speech recognition, which could improve translation services and other language-related technologies. It could also make it easier for people who speak under-represented languages to access technology and services. Additionally, this could open up new opportunities for language learning and research, as it will make it easier to collect data on languages that are not as widely spoken.

Action items

Request API access to the Universal Speech Model (USM) to explore its capabilities.
Research the 300+ languages supported by USM to determine which ones are most relevant to your application.
Experiment with the USM model to evaluate its performance on downstream ASR tasks and automated speech translation.

Technical terms

Universal Speech Model (USM): A family of state-of-the-art speech models with 2B parameters trained on 12 million hours of speech and 28 billion sentences of text, spanning 300+ languages.
Automatic Speech Recognition (ASR): The process of automatically recognizing spoken words and converting them into text.
Word Error Rate (WER): A measure of accuracy in automatic speech recognition, calculated as the percentage of words that are incorrectly recognized.
Whisper (large-v2): A recently released large model, trained with more than 400k hours of labeled data.
Automated Speech Translation (AST): The process of automatically translating spoken words from one language to another.
BLEU Score: A metric for evaluating the quality of text which has been machine-translated from one natural language to another.