Voice & Sound
Applications
Speech recognition
AI-driven speech recognition systems transcribe speech into text for various uses, like voice assistants, call centres, and audio search. NLP is also used to improve the performance of ASR systems by providing context for the spoken language.
Speech synthesis
This technology allows machines to generate natural-sounding speech. Useful applications include voice assistants, creating audiobooks and podcasts from text, and more.
Sound/Audio classification
Sound can be used as a sensor as well. This can be done as part of quality control, as part of a predictive maintenance solution, or directly to detect situations like applause.
Audio enhancement
AI-driven systems can enhance audio quality by removing noise and improving speech clarity. These systems are used in audio editing, podcast production, hearing aids and more.
Typical challenges
Need an AI-driven speech and audio solution for your business? We have you covered. 
Our expertise means we can build solutions that overcome the following challenges:
Accurate speech recognition
Transcribing spoken language into written text can be difficult, especially in noisy environments or when the speech includes different accents, dialects, or domain-specific language. While some models are trained using high-quality audio (e.g., audiobooks), others are designed to handle real-world speech of varying quality and characteristics, making transcription more accessible.
Latency
Transcribing spoken language into written text can be difficult, especially in noisy environments or when the speech includes different accents, dialects, or domain-specific language. While some models are trained using high-quality audio (e.g., audiobooks), others are designed to handle real-world speech of varying quality and characteristics, making transcription more accessible.
Scalability
Scalability is a major challenge for speech AI solutions, because speech recognition and synthesis models are typically large and computationally expensive. However, when building a solution, we always try to meet computational demands while staying within budget. The goal is that data processing never slows down and that scaling is always an option.
Limited data availability
Collecting speech and audio data can be difficult. Annotation is time-consuming and expensive, making it challenging to obtain enough high-quality data to train with. This can lead to model accuracy and robustness issues. But don't worry; our researchers and developers always look for the best way to overcome these obstacles and deliver the best possible results.
High level outline of the solution
The most common applications of speech & audio AI typically involve speech recognition systems to transcribe speech and use them for downstream NLP tasks. Examples include speech summarization, keyword extraction, sentiment analysis, etc.
To make this easier to understand, we've highlighted some key steps for building speech-to-text solutions.
When pre-existing models perform poorly due to previously mentioned challenges, they need to be fine-tuned. The first step is to collect and transcribe relevant real-world data. We do this by using open-source labeling tools.
To make sure that the collected dataset is ready for training, it must be preprocessed. This includes using various noise removal and audio enhancement techniques.
Using preprocessed data, the machine learning model is trained to transcribe audio recordings into text. The performance of the model is then tested and fine-tuned for improvements. The trained model helps to speed up the iterative labeling process and improve performance.
After training and testing, the model is deployed to a production environment where it can transcribe new audio recordings. It's constantly monitored to make sure it remains accurate and up-to-date with changing requirements, either on-edge or in the cloud.
Connect with our experts in AI-powered Voice and Sound solutions
Contact us to learn how our customized AI solutions for audio and speech can improve your business operations, boost productivity, and enhance the user experience. Let our team help you meet the demands of modern communication.