What is Speech Enhancement?
Speech Enhancement is a collection of AI techniques that improve the quality and clarity of audio recordings by removing background noise, reducing echo, compensating for poor microphone quality, and isolating the target speaker's voice. It ensures that speech is clear and intelligible for both human listeners and downstream AI systems.
What is Speech Enhancement?
Speech Enhancement refers to AI-powered techniques that improve the quality of speech audio by suppressing unwanted sounds and amplifying the desired speech signal. When you are on a video call in a noisy cafe and the other person hears you clearly, or when a voice assistant understands your command despite traffic noise in the background, speech enhancement technology is at work.
The goal of speech enhancement is straightforward: make speech clearer. This benefits both human listeners (improving communication quality in calls, meetings, and media) and AI systems (improving the accuracy of speech recognition, speaker recognition, and other audio processing tools that perform better with clean input).
How Speech Enhancement Works
Modern AI-based speech enhancement has largely replaced traditional signal processing approaches with deep learning models that can distinguish between speech and noise with remarkable precision:
- Audio analysis: The system analyses the incoming audio signal, decomposing it into frequency components across time
- Speech and noise separation: A neural network, trained on thousands of hours of clean and noisy speech, identifies which parts of the signal correspond to the target speaker's voice and which correspond to noise, echo, or other speakers
- Signal reconstruction: The system reconstructs the audio, preserving the speech components while suppressing or removing the noise components
- Post-processing: Additional processing may include normalising volume levels, reducing reverb, and applying noise gating for the cleanest possible output
Types of Speech Enhancement
- Noise suppression: Removing background sounds like traffic, air conditioning, keyboard clicking, and construction noise while preserving speech
- Echo cancellation: Eliminating acoustic echo caused by speaker sound being picked up by the microphone, a common problem in conference calls and hands-free systems
- Dereverberation: Reducing the hollow, echoey quality caused by sound reflecting off walls and surfaces in large or hard-surfaced rooms
- Bandwidth extension: Improving the perceived quality of narrowband audio (like phone calls) by artificially adding the frequency components that were removed by compression
- Source separation: Isolating a specific speaker's voice from a mix of multiple speakers, sometimes called the "cocktail party" problem
- Automatic gain control: Normalising audio levels so that quiet speakers are amplified and loud speakers are attenuated
Business Applications of Speech Enhancement
Remote Work and Video Conferencing
- Real-time noise cancellation during video calls, removing barking dogs, construction sounds, and household noise that has become ubiquitous with remote work
- Echo reduction for conference room systems where speakerphone audio creates feedback loops
- Voice clarity enhancement for participants using low-quality laptop microphones or mobile devices
Contact Centres
- Improving call audio quality to enhance customer experience and reduce miscommunication
- Cleaning up recorded calls before they are processed by speech recognition and analytics systems, significantly improving transcription accuracy
- Removing agent-side background noise in home-based contact centre operations
Speech AI Pipeline
- Pre-processing audio before it enters ASR systems to improve transcription accuracy by 15-40% in noisy environments
- Enhancing audio quality before speaker recognition to improve identification accuracy
- Cleaning up training data for speech AI models, ensuring models learn from clear examples
Media and Content Production
- Cleaning up interview recordings, podcast episodes, and video dialogue captured in less-than-ideal acoustic environments
- Restoring audio quality in legacy recordings and archival materials
- Improving the quality of user-generated content for platforms and marketplaces
Surveillance and Security
- Enhancing audio captured by security cameras and monitoring equipment in noisy public spaces
- Improving the intelligibility of recordings used as evidence in investigations
- Enabling clearer communication through intercom and public address systems in noisy environments
Speech Enhancement in Southeast Asia
Speech enhancement is particularly relevant in the ASEAN context for several reasons:
- Tropical and urban noise environments: Southeast Asian business environments frequently contend with unique noise challenges including heavy tropical rain, dense urban traffic, construction, and open-air office or retail environments. AI-based noise suppression tailored to these environmental sounds is highly valuable.
- Remote work growth: As remote and hybrid work models expand across ASEAN, particularly in tech hubs like Singapore, Kuala Lumpur, and Jakarta, speech enhancement ensures productive communication for workers in diverse home environments.
- Contact centre operations: The Philippines, Malaysia, and other ASEAN nations host major contact centre operations. Speech enhancement is essential for maintaining call quality, especially as many agents now work from home where acoustic conditions cannot be controlled.
- Mobile-first communications: With many business interactions happening via mobile phones rather than dedicated VoIP equipment, speech enhancement compensates for the inferior microphone quality and varied acoustic environments typical of mobile calls.
Common Misconceptions
"Speech enhancement creates audio out of nothing." Speech enhancement can only work with what is present in the recording. If speech is completely drowned out by noise, no enhancement system can recover it. The technology separates speech from noise rather than inventing speech that was not captured.
"You need specialised hardware for speech enhancement." While dedicated hardware can help, modern AI-based speech enhancement runs effectively in software on standard devices. Many applications process audio in real time on smartphones and laptops without any special equipment.
"Enhancement always improves things." Aggressive noise suppression can introduce artefacts that make speech sound unnatural or "underwater." Good speech enhancement systems balance noise reduction with speech naturalness, and the optimal settings vary by use case.
Getting Started with Speech Enhancement
- Identify where audio quality is a bottleneck in your communications, customer service, or AI pipelines
- Test real-time enhancement tools like Krisp, NVIDIA RTX Voice, or platform-built-in features for immediate improvements in video conferencing
- Evaluate API-based solutions from providers like Dolby.io and Deepgram for integration into your applications and workflows
- Measure the impact on downstream processes, particularly ASR accuracy improvements, which are often the strongest business case for speech enhancement
- Consider edge versus cloud processing based on latency requirements and data privacy constraints
Speech Enhancement is the often-overlooked foundation that makes other speech AI technologies work effectively. ASR accuracy, speaker recognition reliability, and voice assistant performance all degrade significantly in noisy, real-world conditions. Speech enhancement bridges the gap between the clean audio that AI models are optimised for and the messy audio that real business environments produce.
For CEOs, the business case is both direct and indirect. Directly, speech enhancement improves communication quality in customer calls and internal meetings, reducing misunderstandings, repeat requests, and frustration. Indirectly, it amplifies the return on investment of every other speech AI technology you deploy by ensuring those systems receive clean, high-quality input.
For CTOs, speech enhancement should be considered a prerequisite rather than an optional add-on in any speech AI architecture. Deploying ASR or speaker recognition without adequate speech enhancement is like building a camera system with dirty lenses. In Southeast Asia, where business communications often occur in acoustically challenging environments, from open-plan offices in tropical climates to mobile phones in bustling urban centres, speech enhancement can improve ASR accuracy by 15-40% and make the difference between a speech AI deployment that works in the lab and one that works in the field.
- Evaluate speech enhancement as part of your broader speech AI architecture rather than in isolation. The primary value often comes from improving the accuracy of downstream ASR and analytics systems.
- Test with audio representative of your actual environments. Enhancement systems perform differently with different types of noise, and a system that handles office chatter well may struggle with industrial machinery noise.
- Balance noise suppression aggressiveness with speech naturalness. Over-aggressive noise removal can introduce artefacts that make speech sound robotic or muffled, which may be worse than the original noise.
- Consider latency requirements. Real-time applications like live calls need enhancement with less than 40 milliseconds of delay, while batch processing of recordings can use more computationally intensive approaches for better quality.
- Account for the impact on speaker recognition and diarization. Some enhancement algorithms can inadvertently remove vocal characteristics that are important for identifying speakers.
- Evaluate whether edge processing or cloud processing better suits your needs. Edge processing offers lower latency and better privacy, while cloud processing offers more computational power for higher quality enhancement.
Frequently Asked Questions
How much does speech enhancement improve ASR accuracy?
The improvement depends heavily on the noise conditions. In moderately noisy environments (typical office or home settings), speech enhancement typically improves ASR word accuracy by 10-20 percentage points. In highly noisy environments (busy streets, factories, crowded spaces), improvements of 25-40 percentage points are common. For clean audio, enhancement provides minimal improvement and may occasionally introduce slight artefacts. The general rule is that the noisier the original audio, the greater the benefit from enhancement processing.
Can speech enhancement work in real time on standard devices?
Yes, modern AI-based speech enhancement runs in real time on standard laptops, smartphones, and tablets without specialised hardware. Applications like Krisp and NVIDIA Broadcast process audio with less than 20 milliseconds of latency on consumer devices. Built-in enhancement features in Zoom, Microsoft Teams, and Google Meet also run on standard hardware. For server-side processing of recorded audio, cloud APIs from providers like Dolby.io can enhance audio files at scale without any special infrastructure.
More Questions
Modern AI-based speech enhancement can effectively remove most types of stationary and non-stationary noise including traffic sounds, air conditioning hum, keyboard typing, dog barking, construction noise, crowd chatter, and wind. It can also reduce echo and reverberation from room acoustics. The most challenging scenarios involve noise that is spectrally similar to speech, such as background conversations or television audio, where the system must distinguish between the target speaker and other human voices. Music removal is moderately effective but may introduce more artefacts than environmental noise removal.
Need help implementing Speech Enhancement?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how speech enhancement fits into your AI roadmap.