Speaker Recognition and Identification software offers convenience and an additional layer of security. However, finding the best speaker recognition software can be challenging. Performance, Platform Support, Compliance, Language Dependency, Ease of Use, Developer-Friendliness, Availability of Support, and the Total Cost of Ownership are crucial to choosing the best Speaker Recognition software.
1. Performance
First and foremost, a Speaker Recognition and Identification engine should get the job done. Hence, every vendor claims the “highest” accuracy or the “best” Performance. They may be wrong because measuring speaker recognition performance has nuances. For example, test data (languages, accents, gender) and environment (noise, recording quality, distance to audio source) affect the Performance results. Also, an engine can achieve a low FAR at the cost of a high FRR. The best way to evaluate speaker recognition engine performance is to run tests with your data in your environment instead of relying on vendor claims.
2. Platform Support
Speaker Recognition and Identification engines should support all the Platforms where users interact with your product. It could be mobile, web, call center computers, or embedded devices. It should support all current and future Platforms. Thus, assessment should include the ability and agility of the vendor to stay up-to-date with new Platforms and SDKs.
3. Compliance
Like any other software, Speaker Recognition and Identification engines should comply with Regulations. Regulations can be geographical such as GDPR, or industrial, such as HIPAA. Regulatory Agencies have an eye on the advances in AI as it gets harder to manage vendors. Thus, understanding how vendors store and process users’ voice data is crucial to be prepared. The best and easiest way to protect user data is not to share it with 3rd parties. On-device voice processing offers Privacy, hence Compliance by limiting the 3rd party access to voice data.
4. Language Dependency
Speaker Recognition and Identification engines do not perform when there is a language mismatch if they have language dependency. It’s not a problem for enterprises serving customers speaking only one dialect. However, in today’s world, it’s not very likely. Multinational enterprises should choose a Language-Independent Speaker Recognition model to be inclusive.
5. Ease of Use
Speaker Recognition and Identification promises convenience for the end users. However, some of them may not deliver it fully. Complex enrollment processes or text dependency requiring users to remember passphrases may affect the User Experience adversely. Thus, enterprises should evaluate the Speaker Recognition and Identification enrollment and identification processes as a part of the overall experience.
6. Developer-friendliness
Changing the technology stack just to add Speaker Recognition and Identification due to a lack of SDK support or delaying time-to-market due to integration challenges may cost enterprises more than the actual cost of the software. If enterprises have challenges embedding Speaker Recognition and Identification into their current software, they may need to acquire new talents or lose their first-mover advantage and competitive edge.
7. Availability of Support
IT departments hope for the best and plan for the worst. The availability of technical Support is crucial when things go wrong. The downtime and loss of sales may hurt the continuity of businesses. Thus, enterprises should evaluate how critical the feature is and request support from vendors accordingly.
8. Total Cost of Ownership
Last but not least, the Cost of a Speaker Recognition and Identification engine is an important determinant. Microsoft charges separately for voice profile storage, speaker verification, and speaker identification. The first is based on the number of profiles, and the last two are the number of transactions. Some services are on hourly usage. Opportunity Costs, such as time-to-market, infrastructure Costs, or downtime, affect the real Cost to the business.
Eagle Speaker Recognition checks all the boxes. It is:
- highly accurate
- cross-platform
- private & secure
- language-agnostic
- easy-to-use
- developer-first
- readily available
- cost-effective at scale and over time
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