What you say isn’t everything. It’s all in the delivery, after all. This proverb beautifully summarizes the importance of clear and open communication amongst all members of society. Due to the future that voice and sound technology have projected, it will be necessary for machines to communicate with humans.
The proliferation of IoT devices and AI software has acted as a major catalyst in the rise in popularity of voice communication. Changes in product availability and consumer behavior as a result of AI endpoint integration and advancements in voice analytics have given rise to a new ecosystem of companies that participate in and enable these goods. By facilitating the deployment of both online and offline systems, intelligent endpoint solutions are helping to lessen the need for constant access to the internet or cloud storage. As a result, there are now more ways than ever before to address issues with real-time voice analytics across a wide range of commercial and consumer settings. Data-driven voice models may now infer sentiment, attitude, and intent thanks to developments in psycholinguistic data analytics and affective computing. As speaking to one another becomes more second nature, advances in voice analytics and voice recognition will help us better understand the motivations behind people’s actions.
Challenges of Using VUIs
Voice user interfaces (VUIs) enable the user to control the endpoint device by speaking to it. Although VUIs have seen widespread use in many different contexts, they do have significant drawbacks.
- Poor sound quality: Inconsistent sound quality with continued background noise can make voice recognition a challenge. Voice controllers in IoT can only operate flawlessly if the sound is crystal clear, which is a formidable task in a noisy environment. A voice-enabled assistant can only be truly effective if it is able to support different languages and accents, as well as isolate the human voice from the background noise.
- Power consumption: Voice Command systems are restrictive as they require the activation of at least one microphone as well as the processor that recognizes the wake word.
- Real-time processing: Slow or congested networks can result in command latencies that can impact the user experience. This issue may be addressed by implementing distributed intelligence at the endpoint with the ability to process the voice command in real time without any reliance on the centralized cloud system.
- Accuracy and noise immunity: Voice recognition accuracy and background noise immunity are always major concerns when designing any VUI system. Voice recognition presents a number of challenges as there can be multiple sound sources, including interior and exterior noise and echoes from surfaces in the room, etc. Isolating the source of a command, canceling echoes, and reducing background noise require sophisticated technology depending on multiple microphones, beamforming, echo cancellation, and noise suppression.
Renesas Electronics provides general-purpose MCUs enabling VUI integration without compromising performance and power consumption.
Requirements for Robust Voice Recognition
To make the experience compelling for the user, devices need to be equipped with several components to ensure robust voice recognition.
Command Recognition
The capacity to recognize spoken instructions is a key function of any voice-enabled device. The input is taken in, interpreted, and converted to text by the device’s spoken command recognition system once the wake word has been spoken. Ultimately, this language serves as the input or instruction to carry out the designated action.
Voice Activity Detection
Distinguishing human speech from an audio signal and background noise is the goal of voice activity detection (VAD). Otherwise, the system would need to remain active at all times, resulting in excessive power consumption. VAD is used to improve the optimization of total system power usage. There are four distinct phases to the VAD method.
The VAD algorithm’s four steps—noise minimization, segregation, classification, and response—are laid out in a block diagram. (Renesas Electronics is the source)
Built on the RA MCU family and partner-enabled speech recognition MW, the Renesas RA voice command solution features an advanced noise reduction method that contributes to the solution’s superior VAD accuracy. Furthermore, Renesas can aid in addressing the following essential voice command features:
Keyword Spotting
One of the most important aspects of any voice-enabled device is the keyword spotting system (KWS). The KWS uses voice recognition technology to decipher the terms. These words set off the endpoint’s identification process, letting the audio track match up with the rest of the inquiry.
As shown in the diagram, voice recognition is used in the process of identifying keywords, with the recognized keywords serving as a trigger and commencing the recognition process at the end point so that the audio can match the rest of the inquiry. (Renesas Electronics is the source)
The KWS needs to be able to respond quickly and precisely in real time if it is to help provide a better hands-free user experience. Because of this, the KWS power budget is severely impacted. For this reason, Renesas makes available to its partners high-performance optimized machine learning (ML) models that may be deployed on cutting-edge 32-bit RA microcontrollers. They have pre-trained DNN models that greatly improve keyword spotting accuracy.
Speaker Identification
The task of determining which of several registered speakers produced a certain audio input is known as “speaker identification” (Figure 3). There are three distinct types of speaker recognition: those that require, “learn,” or “prompt” text. Dialect, pronunciation, prosody (rhythmic patterns of speech), and phone use are only some of the characteristics that are used to train the DNN for speaker identification.
Voice/Sound Anti-Spoofing
Spoofing is a form of impersonation fraud in which the perpetrator poses as another user in order to obtain access to a protected system. To prevent this, the system should have anti-spoofing software built in. Automatic Speaker Verification (ASV) devices are a common target of spoofing assaults (Figure 4). Spoofing speech can be created using speech synthesis, voice conversion, or even just the repetition of recorded speech. Depending on the nature of their interaction with the ASV infrastructure, these attacks are either direct or indirect.
- Direct Attack: This can occur through the sensor at the microphone and transmission level and is also known as Physical Access.
- Indirect Attack: This is an intrusion into the feature extraction, models, and the decision-making process of the ASV system software and is also known as Logical Access attack.
Multi-Language/Accent Recognition and Understanding
Because of the abundance of training data, recognizing English speakers’ accents is a significantly simpler procedure in English-speaking countries. With fewer data available for training, voice recognition becomes less accurate for businesses operating in countries where English is not the primary language. A lack of sufficient training data makes it difficult to construct conversational models with high accuracy.
Renesas provides a VUI partner-enabled solution to the problem of accent recognition by supporting more than 44 languages. This makes it a highly flexible speech recognition system that can be utilized by any company, wherever in the globe.
