Review - AI tools for speech and voice processing
Thanks to rapid advances in artificial intelligence, speech and voice processing tools have become an integral part of modern applications. From voice assistants to automated captioning and transcripts, AI can process and analyze speech with ever-increasing accuracy.
This review will focus on the most popular AI tools for speech and voice processing,, their advantages, disadvantages, and practical uses.
1. Categories of AI speech and voice processing tools
Modern tools in this fields are divided into several main categories :
1.1 Automatic Speech-to-Text (ASR)
➡ Application: transcription of the spoken word into text (e.g. subtitles, voice commands, transcription of recordings).
1.2 Text-to-Speech (TTS) speech generation
➡ Use: conversion of text into spoken word (e.g. voice assistants, audiobooks, synthetized speech).
1.3 Voice and Sentiment Analysis
➡ Use: Identification of emotion and intonation in the voice (e.g. call centres, customer interaction analysis).
1.4 Real-time speech translation
➡ Use: Automatic translation of the spoken word (e.g. simultaneous interpreting, language assistant).
2. Overview of the best AI tools for speech processing
2.1 Google Speech-to-Text
✅ Advantages:
- Support for more than 125 languages
- Automatic punctuation
- Ability to train models for specific voices
❌ Disadvantages:
- May have problems with dialects and non-standard accents
- Paid version for larger amounts of data
Example: YouTube uses Google Speech-to-Text to generate automatic subtitles for videos.
2.2 Microsoft Azure Speech Services
✅ Benefits:
- High accuracy in different domains (medicine, law, IT)
- Ability to create custom speech recognition models
- Strong integration with Microsoft products
❌ Disadvantages:
- Requires Azure subscription
- Slightly higher latency in some applications
Example: Companies use Azure Speech Services to automatically transcribe phone conversations in call centers.
2.3 Amazon Transcribe
✅ Benefits:
- Automatic identification of multiple speakers
- Integration with the AWS ecosystem
- Ability to customize the dictionary for specific industries
❌ Disadvantages:
- Less effective with background noise
- May have higher costs for longer recordings
Example: Amazon Transcribe is used to automatically generate transcripts for podcasts and audiobooks
2.4 OpenAI Whisper
✅ Benefits:
- One of the most accurate speech transcription technologies
- Works offline and open-source
- Supports multiple languages and transcribes even with dialect variations
❌ Nevýhody:
- Higher computational requirements
- Does not have built-in commercial support like cloud-based solutions
Example: Journalists use OpenAI Whisper to transcribe interviews and press conferences.
2.5 ElevenLabs (Text-to-Speech, AI voice generation)
✅ Benefits:
- Realistic synthesized speech with natural intonations
- Voice cloning capability
- Suitable for audiobooks and podcasts
❌ Disadvantages:
- Some advanced features are only available in the paid version
- Ethical issues related to vote generation
Example: audiobook creators use ElevenLabs to generate professional-sounding synthesized speech.
2.6 IBM Watson Speech-to-Text
✅ Benefits:
- Powerful analytics and sentiment analysis
- Suitable for enterprise applications
- Ability to customize models for specific industries
❌ Disadvantages:
- More complex to configure compared to competitors
- Fewer languages than Google or Azure
Example: Banks use IDM Watson to analyze customer phone calls and detect dissatisfaction based on tone of voice.
3. Real-world applications of AI for speech and voice processing
3.1 Voice assistants (Google Assistant, Siri, Alexa)
➡ AI recognizes voice commands and responds in real time.
3.2 Automatic appointment transcripts (Zoom, Otter.ai, Notta)
➡ Tools can transcribe appointments and generate notes.
3.3 AI voice cloning (Deepfake Voice, Voicery, ElevenLabs)
➡ The technology is used in the gaming industry, but also carries ethical risks.
3.4 Real-time speech translation (Meta AI, Google Translate, Skype Translator)
➡ Used for simultaneous interpretation in online meetings.
4. Which tool is best for you?
Purpose Recommended tool
Speech-to-text (ASR) OpenAI Whisper, Google Speech-to-Text
Synthesized speech generation(TTS) ElevenLabs, Microsoft Azure Speech
Tone of voice and sentiment analysis IBM Watson Speech, Amazon Transcribe
Automatic speech translation Google Translate, Skype Translator
Voice Cloning ElevenLabs, Voicery
If you need highly accurate transcription, Whisper is a great open-source option. If you´ re looking for an enterprise solution, Azure Speech Services or Amazon Transcribe may be a better choice. For realistic speech generation, ElevenLabs is the current top choice.