Preparation of a dataset for machine learning, in which live speech from audio recordings and videos gets converted into text format.

Speech-to-Text

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Speech transcription for Speech-to-Text AI

Technical assignment template

What goals can you achieve by Speech-to-Text

SERVICES → LABELME.RU

The text transcribing service belongs to the NLP (Natural Language Processing) section. Its essence is in the processing and automation of work with live speech. The main advantage of transcribing speech with people's help is compliance with all spelling and punctuation standards or additional metadata labeling. Automatic algorithms still make mistakes in this.

Transcribing types

Number of speakers

To improve machine learning accuracy, proper annotation is crucial. For example, if there is more than one speaker on the recording, we will not only accurately convert the recording into text but also label the speakers into different classes. Without this, the neural network can convert voice into text but cannot track the change of speaker. It can be a critical disadvantage when solving specific tasks.

Record quality

There were cases when the customer provided us with records of poor quality. Outside noise, microphone friction, speech in the background — we know how to work with all this.
To solve these problems, we added scripts and functions to improve the quality of recordings. It allows our specialists to convert every word accurately. And where technology fails, linguists help us. They listen to the muffled words and restore the context of the spoken phrase.

Speech labeling in different languages

It’s crucial that the specialist knows the language at the level of the native speaker and knows grammatical, lexical, and spelling standards. Otherwise, they will not be able to transcribe the speech recording correctly. Through local media and partner networks, we find native speakers.

Application examples

The LabelMe team is working on data labeling for a wide variety of businesses:

Voice assistants

To create and improve the voice assistants' accuracy, terabytes of correctly annotated data are needed. In addition to extracting entities to understand the meaning of speech, transcription is needed. It allows recognizing and understanding the combinations of sounds, letters, and syllables that create words and sentences. It's the only way the machine will be able to decode human speech correctly.

Call center automation

We have transcribed recordings of human speech to create interactive voice responses (IVR). This technology involves the creation of robots that generate human speech and the automatic conversion of the entire dialogue into a text format. The latter simplifies the work of collecting and analyzing customer behavior for marketing research.

Speech-to-Text systems

An excellent example of using this function is the automatic conversion of voice recordings into text messages in VKontakte. However, even now, this model makes mistakes and incorrectly transcribes some words and phrases. To avoid this, we must process the people's speech with all the different manners of speech and pronunciation features.

Among our orders, there are cases where our team helped to foresee this problem: the client provided too "clean" data, and we helped to diversify the dataset.

Streaming services

Speech transcribing is the basis for creating automatic subtitles and generating metadata for multimedia resources. What people used to have to do can now be put on autopilot. It's especially relevant for live broadcasts or interactive meetings in which speakers speak different languages.
We will prepare a training dataset with transcription in Russian, English, French, German and other languages for you.