Developers across many industries now use automatic speech recognition (ASR) to increase business productivity, application efficiency, and even digital accessibility. This blog discusses ASR, how it works, use cases, advancements, and more.
What is automatic speech recognition?
Speech recognition technology is capable of converting spoken language (an audio signal) into written text that is often used as a command.
Today’s most advanced software can accurately process varying language dialects and accents. For example, ASR is commonly seen in user-facing applications such as virtual agents, live captioning, and clinical note-taking. Accurate speech transcription is essential for these use cases.
Developers in the speech AI space also use alternative technologies to describe speech recognition such as ASR, speech-to-text (STT), and voice recognition.
ASR is a critical component of Speech AI, which is a suite of technologies designed to help humans converse with computers through voice.
Why natural language processing is used in speech recognition?
Developers are often unclear about the role of natural language processing (NLP) models in the ASR pipeline. Aside from being applied in language models, NLP is also used to augment generated transcripts with punctuation and capitalization at the end of the ASR pipeline.
After the transcript is post-processed with NLP, the text is used for downstream language modelling tasks:
- Sentiment analysis
- Text analytics
- Text summarization
- Question answering
ASR Key Terms and Features
Acoustic Model:
The acoustic model takes in audio waveforms and predicts what words are present in the waveform.
Language Model:
The language model can be used to help guide and correct the acoustic models predictions.
Word Error Rate:
The industry standard measurement of how accurate an ASR transcription is, as compared to a human transcription.
Speaker Diarization:
Answers the question, who spoke when? Also referred to as speaker labels.
Custom Vocabulary:
Also referred to as Word Boost, custom vocabulary boosts accuracy for a list of specific keywords or phrases when transcribing an audio file.
Sentiment Analysis:
The sentiment, typically positive, negative, or neutral, of specific speech segments in an audio or video file.
Key Applications of ASR
The immense advances in the field of ASR has seen a correlation of growth in Speech-to-Text APIs. Companies are using ASR technology for Speech-to-Text applications across a diverse range of industries. Some examples include:
Telephony:
Call tracking, cloud phone solutions, and contact centers need accurate transcriptions, as well as innovative analytical features like Conversational Intelligence, call analytics, speaker diarization, and more.
Video Platforms:
Real-time and asynchronous video captioning are industry standard. Platforms also need content categorization and content moderation to improve accessibility and search.
Media Monitoring:
Speech-to-Text APIs can help broadcast TV, podcasts, radio, and more quickly and accurately detect brand and other topic mentions for better advertising.
Virtual Meetings:
Meeting platforms like Zoom, Google Meet, WebEx, and more need accurate transcriptions and the ability to analyse this content to drive key insights and action.
Choosing a Speech-to-Text API
With more APIs on the market, how do you know which Speech-to-Text API is best for your application?
Key considerations to keep in mind include:
- How accurate the API is.
- What additional features are offered.
- What kind of support you can expect.
- Pricing and documentation transparency.
- Data security.
- Company innovation.
Challenges of ASR Today
One of the main challenges of ASR today is the continual push toward human accuracy levels. While both ASR approaches–traditional hybrid and end-to-end Deep Learning–are significantly more accurate than ever before, neither can claim 100% human accuracy. This is because there is so much nuance in the way we speak, from dialects to slang to pitch. Even the best Deep Learning models can’t be trained to cover this long tail of edge cases without significant effort.
Some think they can solve this accuracy problem with custom Speech-to-Text models. However, unless you have a very specific use case, like children’s speech, custom models are actually less accurate, harder to train, and more expensive in practice than a good end-to-end Deep Learning model.
Another top concern is Speech-to-Text privacy for APIs. Too many large ASR companies use customer data to train models without explicit permission, raising serious concerns over data privacy. Continual data storage in the cloud also raises concerns over potential security breaches, especially if raw audio or video files or transcription text contains Personally Identifiable Information.