ASR: What is the Automatic Speech Recognition?

Automatic Speech Recognition is an automatic word conversion method into text. ASR technologies use automatic learning procedures to analyze language models, process them and display in text form. The Automatic Speech Recognition lends itself to a multitude of applications, ranging from virtual vocal assistants to the creation of subtitles for videos, including the transcription of important meetings.

What does Automatic Speech Recognition mean?

Automatic Speech Recognitionin French « automatic speech recognition », is a term designating the IT and IT linguistics field. In fact, it is a question here of developing methods which automatically translate the language spoken into a readable form by a machine. When the conversion is done in text, we also speak of Speech-to-Text (STT). ASR methods are based on statistical models and complex algorithms.

The Automatic Speech Recognition consists of several successive steps which overlap each other. Here are the different phases of the process:

1. Speech (Automatic Speech Recognition): The system captures the language spoken via a microphone or another audio source.
2. Speech treatment (Natural Language Processing): Vocal recording is first cleaned parasitic noises. Then, an algorithm analyzes the phonetic and phonemic characteristics of speech. Finally, the characteristics entered are compared to previously trained models in order to identify individual words.
3. Text generation (Speech to text): The system converts the sounds recognized as text.

ASR algorithms: hybrid approach vs deep learning

We fundamentally distinguish two main approaches for automatic speech recognition: while in the past, we mainly used conventional hybrid approaches such as hidden Markov models, we are now increasingly recourse to technologies of technologies Deep Learning. This situation is explained by the fact that the accuracy of traditional models is currently stagnating.

Classic hybrid approach

Classic models require strength -aligned data. This means that they use the textual transcription of an audio speech segment to determine the place where certain words appear. The traditional hybrid approach always combines a lexicon model, an acoustic model and a linguistic model to transcribe speech:

• THE lexicon model defines the phonetic pronunciation of words. It is necessary to create a set of data or phonemes for each language.
• THE acoustic model aims to represent the sound characteristics of the language. Thanks to data aligned in a forced manner, it predicts the phoneme corresponding to each audio segment, thus making it possible to precisely associate each sound with a linguistic unit.
• THE linguistic model Learn what sequences of words are most likely to appear in a language. His task is to predict what words will follow the current words and with what probability.

The main drawback of the hybrid approach is that it is difficult to increase the Precision of vocal recognition Using this method. It is also necessary to train three distinct models, which is very expensive in time and money. As there are already many knowledge about how to create a robust model using the classic approach, many companies nevertheless opt for this option.

Deep Learning with end -to -end processes

End -to -end systems have the ability to transcribe a series of acoustic characteristics into text directly. The algorithm learns to convert the words pronounced thanks to a large set of data, made up of pairs of audio records of specific sentences and their correct transcriptions.

Deep learning architectures such as CTC, LAS and RNNT can be drawn to provide precise results even in the absence of forced data, lexicon model and linguistic model. Many Deep Learning systems are nevertheless associated with a linguistic model, as the latter can help further improve the accuracy of the transcription.

The end -to -end approach of the Automatic Speech Recognition is not only distinguished by greater precision than traditional models. It also has the advantage of Facilitate the training of ASR systems and reduce the necessary workforce.

What are the main fields of application of the Automatic Speech Recognition?

Thanks in particular to the progress made in the field of machine learning, ASR technologies are becoming more and more precise and efficient. The Automatic Speech Recognition can be used in many sectors in order to carry out efficiency gains, increase customer satisfaction and/or improve return on investment (king). The main areas of application are:

• Telecommunications : Contact centers use ASR technologies to transcribe conversations with customers and analyze them later. Exact transcriptions are also necessary for calling calls and for telephone solutions made using cloud servers.
• Video platforms : The creation of real-time subtitles on video platforms has become a standard. The Automatic Speech Recognition is also useful for the categorization of content.
• Media surveillance : ASR APIs allow you to analyze television programs, podcasts, radio and other media shows with regard to the frequency of the appearance of certain marks or themes' mentions.
• Videoconferences : Meeting solutions such as Zoom, Microsoft Teams or Google Meet depend on the exact transcription and the analysis of this content to obtain key information and take appropriate measures. The Automatic Speech Recognition can also be used to provide live subtitles for videoconferences.
• Vocal assistants : Whether Amazon Alexa, Google Assistant or Siri d'Apple, virtual voice assistants are based on the Automatic Speech Recognition. This technology allows assistants to answer questions, do tasks and interact with other devices.

What is the role of artificial intelligence in ASR technologies?

Artificial intelligence contributes to improving the precision and general functionality of ASR systems. In particular, the development of large linguistic models has made it possible to considerably improve natural language treatment. A Large Language Model (LLM) is not only capable of producing complex and relevant texts and making translations, but also recognizes spoken language. ASR systems therefore considerably benefit from developments in this area. In addition, artificial intelligence is also useful for the development of specific linguistic models.

What are the ASR strengths and weaknesses?

Compared to traditional transcription, automatic speech recognition has certain advantages. One of the main strengths of modern methods of automatic voice recognition lies in their great precision, due to the fact that the corresponding systems can be trained with large amounts of data. This improves the quality of subtitles or transcriptions and provide them in real time.

Another important advantage is the increase in efficiency. The Automatic Speech Recognition allows companies to evolve, to broaden their range of services more quickly and to offer them to a larger number of customers. For students and professionals, automatic voice recognition tools facilitate audio content documentationfor example of a business meeting or a university course.

The drawback is that automatic voice recognition systems, although more precise than ever, still do not reach the precision of humans. This is mainly due to the many shades of speech. The accents, dialects and different tones, but also parasitic noises, constitute a particular challenge. Even the most efficient deep learning models cannot cover all special cases. Another problem: ASR technologies sometimes deal with personal datawhich raises concerns about privacy and data security.