Transforming Your Siri Experience with the Revolutionary Voice Trigger System
Introduction:
A growing number of consumer devices now rely on voice as the primary means of user input. Voice trigger detection systems have become crucial in facilitating seamless interactions between users and devices. Apple has designed an on-device voice trigger system that ensures high accuracy, privacy, and power efficiency across various Apple devices. This article delves into the challenges faced and solutions implemented in the design of this system. The system architecture consists of multiple stages, including a streaming voice trigger detector and a high-precision voice trigger checker. Additionally, the article discusses the implementation of a personalized voice trigger system to reduce false triggers from non-primary users.
Full Article: Transforming Your Siri Experience with the Revolutionary Voice Trigger System
Apple Designs High-Accuracy On-Device Voice Trigger System
A growing number of consumer devices are utilizing voice recognition technology as the primary means of user input. To support this interaction, voice trigger detection systems have become crucial in controlling access to devices or features. Apple has designed a high-accuracy, privacy-centric, power-efficient, on-device voice trigger system to enable natural voice-driven interactions with their devices.
Supporting Multiple Apple Device Categories
Apple’s voice trigger system supports various device categories including iPhone, iPad, HomePod, AirPods, Mac, Apple Watch, and Apple Vision Pro. Two keywords, “Hey Siri” and “Siri,” are simultaneously supported for voice trigger detection on these devices.
Addressing Voice Trigger Detection Challenges
This article discusses four specific challenges Apple has addressed in their voice trigger detection system:
1. Distinguishing the primary user from other speakers
2. Rejecting false triggers from background noise
3. Rejecting acoustic segments similar to trigger phrases
4. Supporting a shorter phonetically challenging trigger phrase across multiple locales
Voice Trigger System Architecture
The architecture of Apple’s voice trigger system consists of multiple stages. On mobile devices, audio is analyzed in a streaming fashion on the Always On Processor (AOP) and stored in an on-device ring buffer. A streaming high-recall voice trigger detector system analyzes the user’s input audio and discards any audio that doesn’t contain the trigger keywords. Audio potentially containing the trigger keywords is analyzed on the Application Processor (AP) using a high-precision voice trigger checker system. For personal devices like iPhone, the speaker identification (speakerID) system determines if the trigger phrase is uttered by the device owner or another user. Siri directed speech detection (SDSD) analyzes the full user utterance, including the trigger phrase segment, to mitigate any potential false voice trigger utterances.
Streaming Voice Trigger Detector
The first stage of the voice trigger detection system is a low-power, first-pass detector that utilizes a deep neural network (DNN) hidden markov model (HMM)-based keyword spotting model. The DNN predicts the state probabilities of speech frames, while the HMM decoder combines the DNN predictions to compute the keyword detection score. The DNN output contains 23 states including phonemes, silence, and background states. The DNN model is fine-tuned through end-to-end training to optimize for detection scores. Furthermore, Apple employs advanced palettization techniques to compress the DNN model for efficient inference on mobile devices.
High Precision Conformer-Based Voice Trigger Checker
If a detection is made at the first pass, larger models are used to re-score the candidate acoustic segments. Apple utilizes a conformer encoder model with self-attention and convolutional layers, which provides better accuracy compared to other architectures. The encoder part of the network is used during inference to prevent sequential computations in the autoregressive decoder, resulting in efficient training and inference times. Additionally, a relatively small amount of trigger phrase-specific discriminative data is added to further improve the model’s performance.
Personalized Voice Trigger System
To reduce false triggers from users other than the device owner, Apple personalizes each device. This personalization ensures that unintended activations occur less frequently when similar-sounding phrases are spoken by the primary user or other users. By personalizing the device, Apple enhances the accuracy and effectiveness of their voice trigger system.
Conclusion
Apple’s high-accuracy, privacy-centric, power-efficient voice trigger system enables natural voice-driven interactions with their devices. Through innovative design and addressing specific challenges, Apple has created a sophisticated on-device voice trigger system that optimizes privacy, latency, accuracy, and power consumption.
Summary: Transforming Your Siri Experience with the Revolutionary Voice Trigger System
A growing number of consumer devices are using voice as the primary means of user input. This has led to the development of voice trigger detection systems, which are used to control access to devices or features. In this article, we will discuss how Apple has designed a high-accuracy, privacy-centric, power-efficient voice trigger system for their devices. We address four specific challenges of voice trigger detection and explain the architecture of the system. We also discuss the use of advanced techniques like neural networks and speaker recognition to improve accuracy and reduce false triggers.
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