Audio Hardware Configurations • • • • • • • Overview Alexa Voice Service (AVS) is Amazon's intelligent cloud service that allows you to voice-enable connected products that have a microphone and speaker. By integrating AVS, your users immediately gain access to Alexa's core capabilities and a growing library of third-party skills. Alexa users expect a familiar experience. Use the design and implementation guidance below to ensure that your AVS integration meets user expectations: • – Identify your product's form factor and choose how users will interact with Alexa. • – Determine the best automatic speech recognition (ASR) profile for your implementation. • – Review techniques used to optimize speech recognition.

Reliable acoustic echo cancellation software already deployed in many devices from installed audio to mobile devices. Providing Reliable Signal Processing Solutions Since 2001. Our acoustic echo cancellation solutions are used in all communication and conferencing markets where an AEC has an added value, from large multi-microphone.

• – Learn about providers that offer hardware and software solutions. As you integrate AVS, please make sure to reference our. Typical Application Examples Alexa allows users to interact with products in the most natural way possible – with their voice. Whether you're building a hand-held device like a TV remote, a wearable like a smart watch, or you want a hands-free experience for your connected speaker or home intercom, AVS provides a way for your users to speak to Alexa. There are two ways to initiate an interaction with Alexa: touch and voice. Touch-initiated interactions rely on a physical control like the Amazon Fire TV remote or Amazon Tap.

Voice-initiated interactions leverage the wake word 'Alexa', like Amazon Echo. This table highlights typical application examples for push-to-talk, tap-to-talk and voice-initiated products: Application Push-to-talk Tap-to-talk Voice-initiated (Wake Word) Remotes ● Wearables ● ● Mobile Apps ● ● ● Portable Speakers ● ● Home Audio ● ● Intercoms ● ● Smart Home / Appliances ● ● ● Automotive ● ● Personal Computers ● ● Smart TV / Set Top Boxes ● Automatic Speech Recognition Profiles Alexa uses a combination of automatic speech recognition (ASR) and natural language understanding (NLU) to understand user speech and respond with precision.

ASR converts user speech into text, NLU converts that text into intents for Alexa to act on. At the end of this process Alexa sends directives to your product instructing it to perform an action, like playing music. AVS provides a choice of three ASR Profiles tuned for different products, form factors, acoustic environments and use cases.

The profile parameter is sent to Alexa in the payload of each and is also used to indicate if the end of user speech will be determined by your client or in the cloud (how the end of speech is determined is called speech endpointing). The following table highlights which ASR Profiles are commonly associated with different user interactions. Push-to-talk Tap-to-talk Voice-initiated (Wake Word) Listening Range Up to 2.5 ft. ASR Profile Speech Endpointing Client Cloud Cloud Cloud Hardware and Audio Algorithms The correct hardware configuration and audio processing algorithms can improve your product's listening sensitivity for the wake word Alexa. This is especially true if your product is designed for music playback or intended for use in noisy environments.

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Any type of nonlinear processing on the audio input, such as traditional noise reduction algorithms or automatic gain controls, should not be used. The following table highlights typical configurations: Push-to-talk Tap-to-talk Voice-initiated (Wake Word) Listening Range Up to 2.5 ft. Wake Word ● ● # of Microphones 1 1 1+ 2+ AEC ● ● Beamforming >2 Microphones >2 Microphones Acoustic Echo Cancellation (AEC) In speech recognition systems, the term “acoustic echo” refers to the signal that is played out of a loudspeaker and captured by a microphone in the vicinity of the loudspeaker. The acoustic echo is a source of interference for the ASR engine since it is simultaneously captured along with the user’s voice at the microphone. The goal of AEC is to remove the acoustic echo component from the microphone signal, so that the user’s voice can be clearly understood by the ASR engine. The AEC algorithm functions by adaptively estimating the acoustic echo path (and thereby the acoustic echo) between the loudspeaker and microphone components.