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Building Speech-to-Text Applications

Businesses are adopting automatic speech recognition technology to save time and improve productivity. Voice search and virtual assistants are common examples of speech-to-text (STT) applications.

Modern speech recognition software can quickly and accurately transcribe audio to text. Rapid improvements in STT software have furthered the application of this technology beyond obvious use cases. From healthcare to product marketing, speech-to-text systems are now being leveraged across verticals.

Speech-to-Text Applications in Healthcare

The healthcare industry is one which can benefit immensely from voice recognition software. Speech recognition technology eliminates the need to manually transcribe clinician’s notes. Clinicians dictate notes and AI-powered STT software can accurately convert the voice data into useful EHR data.

This leads to a significant improvement in clinical documentation and its accuracy. At the same time, physicians are able to dedicate more time to care delivery without bothering about EHR data entry.

Speech Analytics Aiding Customer Service

Call recordings from contact centers can be converted into text format with the aid of speech recognition technology. Once speech-to-text conversion is done, the transcripts can be mined to understand customer intent and devise better service strategies.

Analysis of individual call transcripts provides deeper insight into the customer’s needs, opinions, problems, and satisfaction drivers. Aggregated analysis of the interaction repository helps assess the quality of the contact center and optimize operations.

Automatic Transcription for Podcast Monetization

STT technology is core to gaining insight into podcast content and monetizing podcasts with contextually targeted ads.

With intelligent transcription engines, advertisers can target individual episodes instead of high-level targeting based on show topic or podcast category. The capability opens up greater monetization opportunities.

Lead Attribution and Scoring with Speech Analytics

AI-powered voice recognition software can categorize data retrieved from voice calls into high/low intent or missed opportunity providing marketers with accurately classified leads.

Machine learning models are trained on the data from previous calls to assign scores against each new call. Based on the data from past leads with similar behavior, custom predictors can be built allowing for accurate and faster lead scoring.

Converting Speech to Text

Speech recognition systems adopt approaches varying from simple pattern matching to statistical modeling and artificial neural networks.

The primary step in speech-to-text conversion is to convert the audio signal into digital format. A pattern matching system compares the graphs of saved words with incoming data to decipher the audio. This may work with a limited vocabulary but more complex methods such as feature analysis are often necessary for the huge vocabulary involved in typical human speech.

By mathematically analyzing the language to find patterns, we can build a statistical model for how a language works. While such language models are widely in use, modern speech recognition systems employ artificial neural networks to achieve greater accuracy.

Speech-to-Text Services from Cloud Platforms

Integrating voice recognition into your application is simplified with the aid of numerous speech recognition APIs available today. You can build highly accurate STT converters using such managed speech APIs.

All the three major cloud platforms offer APIs for adding speech-to-text capabilities to applications but your choice is not limited to them. Many of the speech APIs apply deep learning algorithms and artificial neural networks to accurately process speech into text with automated punctuation and formatting.

Google Cloud Speech-to-Text

Amazon Transcribe

MS Azure Speech-to-Text

You can use the API services to transcribe in real time or batches, detect and convert multiple languages to text, create domain-specific language models, or filter out specific words from the transcribed text. With some providers offering flexible deployment options, your speech-to-text systems can run on the cloud or on premises.

Open Source Speech Recognition Libraries

Web Speech API

JavaScript API to enable speech input and text-to-speech output on web pages.

DeepSpeech

STT engine built on TensorFlow framework that can be embedded into devices.

Kaldi

Toolkit written mainly in C++ that works on Windows, Linux, and macOS.

Fairseq

Sequence-to-sequence model based toolkit for complicated language processing.

Vosk

Works offline and provides a streaming API for speech recognition and speaker identification.

Athena

Another sequence-to-sequence automatic speech recognition engine built on top of TensorFlow.

Web Speech API

JavaScript API to enable speech input and text-to-speech output on web pages.

DeepSpeech

STT engine built on TensorFlow framework that can be embedded into devices.

Kaldi

Toolkit written mainly in C++ that works on Windows, Linux, and macOS.

Fairseq

Sequence-to-sequence model based toolkit for complicated language processing.

Vosk

Works offline and provides a streaming API for speech recognition and speaker identification.

Athena

Another sequence-to-sequence automatic speech recognition engine built on top of TensorFlow.

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