Speaker Recognition software offers convenience and an additional layer of security. However, finding the best speaker recognition software can be challenging.
Ease of Use,
Availability of Support, and the
Total Cost of Ownership are crucial to choosing the best
Speaker Recognition software.
First and foremost, a
Speaker Recognition 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 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.
Like any other software,
Speaker Recognition engines should comply with
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
Compliance by limiting the 3rd party access to voice data.
4. Language Dependency
Speaker Recognition 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 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 enrollment and identification processes as a part of the overall experience.
Changing the technology stack just to add
Speaker Recognition 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 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 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
- private & secure
- readily available
- cost-effective at scale and over time
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