SpeechSuper English Speech to Text API Supports Inverse Text Normalization

SpeechSuper released a new speech-to-text (speech recognition) API feature: inverse text normalization.


What is inverse text normalization? It converts the words to numerical or scientific expressions for better readability and understanding. For example, the recognized words 'seventeen dollars' can be converted to '$17' via inverse text normalization.


We now support inverse text normalization in the following domains.

1. Cardinal number and currency

 


SpeechSuper's English speech-to-text API can support number and currency conversion. For example, if the recognized words are 'It costs three hundred and one dollars.', it will be converted to 'It costs $301.'

2. Date


SpeechSuper's English speech-to-text API can support date conversion. For example, if the recognized words are 'I was born on November first nineteen ninety-seven.', it will be converted to 'I was born on November 1, 1997'.

3. Decimal number


SpeechSuper's English speech-to-text API can support decimal conversion. For example, if the recognized words are 'The investment is five point two million dollars.", it will be converted to 'The investment is $5.2 million."

4. Phone number


SpeechSuper's English speech-to-text API can support spelled-out numbers. For example, if the recognized words are 'My telephone number is six two one four zero two five zero.", it will be converted to 'My number is 62140250."

5. Scientific units


SpeechSuper's English speech-to-text API can support scientific units. For example, if the recognized words are 'I weigh fifty-seven kilograms", it will be converted to "I weigh 57 kg."

SpeechSuper's English speech-to-text API can support time expressions. For example, if the recognized words are "It's half past nine p.m.", it will be converted to "It's 09:30 p.m."

SpeechSuper English speech-to-text API has been evolving. You can try the demo here. If you’re interested, please contact us on the website.

----------------------------------------------------------

ABOUT US

Qiusi is a product manager in China’s EdTech industry focusing on language learning and AI. She enjoys writing stories. You can reach her at qiusi.dong@speechsuper.com

SpeechSuper provides cutting-edge AI speech assessment (a.k.a pronunciation assessment or pronunciation score) APIs for language learning products. Comprehensive feedback covers pronunciation score, fluency, completeness, rhythm, stress, liaison, etc. Languages supported include English, Mandarin Chinese, French, German, Korean, Japanese, Russian, Spanish, and more.

*Prior written consent is needed for any form of republication, modification, repost, or distribution of the contents.


Comments

Post a Comment

Popular posts from this blog

How Accurate is Pronunciation Assessment?

Image
Pronunciation assessment APIs usually offer pronunciation scores at phoneme level, syllable level, word level, and sentence level. Yet, how do you know if the scores are accurate? By comparing the predicted pronunciation scores of the testset with the golden standard (human label). The closer the two results, the more accurate the algorithm is.  To make the metric(s) representative and useful, we need to think carefully about 1) testset, and 2) performance metrics. Testset The testset is usually designed by an AI product manager. He/She should ensure that the testset can 1) reflect real user scenarios, 2) have good data variety, and 3) cover a wide range of use cases. For example, SpeechSuper's API testsets consist of masked audios from language learners sampled from our real user base. They usually cover a wide spectrum of phonetic combinations in a specific language. The testsets not only contain data recorded in a quiet environment but also with background noise. Metrics I guess
Read more

DON’T Use Speech Recognition in Language Learning Apps

Image
After researching ~100 language learning apps in South East Asia and the American app market, I found that only 27% allow users to practice speaking, and most of them use speech recognition as speaking feedback.  Using speech recognition is ineffective in language learning for two reasons. 1. Good pronunciations are all alike; every mispronunciation is faulty in its own way. One of the barriers to language learning is the mother tongue, especially for learners 12 years old and above. Deeply influenced by the sound system of their mother tongues, language learners may confuse sounds in their mother tongue with those in a new language. There are over 7000 languages globally, so a single language corresponds to a broad spectrum of mispronunciations from language learners.  It is happy if a recognition system can recognize speech, but what matters the most is how to deal with mispronunciations, which fail the recognition system. It can not shed light on where and how to improve, but those
Read more