You work as a machine learning specialist for major phone and internet providers.
Your customer support department needs to upgrade its phone response systems to reduce the number of human service representatives needed to handle dramatically increasing call volume.
Your senior management team has leveraged off-shore call center services to reduce costs.
Still, they now want to take advantage of voice recognition to automate many of the most frequent support call types, such as “I forgot my password” or “my internet is down.” Your management team has assigned you to the team to implement the machine learning model behind the voice recognition system.
Which SageMaker built-in algorithm is the best choice for this problem?
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: A.
Option A is correct.
The Sequence-to-Sequence algorithm takes audio as input data and generates a sequence of tokens, such as the words in the audio.
This can then be used to provide automated responses to users' requests.
Option B is incorrect.
The K-Means algorithm is used to find groups within data where the group members are similar to each other but different from members of other groups.
This algorithm will not help you encode speech audio streams.
Option C is incorrect.
The semantic segmentation algorithm is used to develop computer vision applications.
You are trying to solve a speech recognition problem.
So this algorithm would not work for this problem.
Option D is incorrect.
The Neural Topic Model algorithm is used to group documents into topics using the statistical distribution of words within the documents.
You are trying to solve a speech recognition problem.
So this algorithm would not work for this problem.
Reference:
Please see the SageMaker developer guide titled Using Amazon SageMaker Built-in Algorithms.
The best choice of SageMaker built-in algorithm for implementing the machine learning model behind the voice recognition system for this problem is the Sequence-to-Sequence algorithm (Option A).
The Sequence-to-Sequence algorithm is a popular machine learning algorithm that is well suited for natural language processing and is often used in speech recognition systems. This algorithm is based on the concept of sequence modeling, which involves input sequences being mapped to output sequences. This algorithm can be used to model sequences of arbitrary length, making it ideal for voice recognition systems where the length of the spoken phrase can vary.
The Sequence-to-Sequence algorithm works by breaking the input sequence into smaller parts and mapping them to a corresponding output sequence. The algorithm uses a deep neural network to learn the mapping between the input sequence and the output sequence. The neural network is trained using a large dataset of labeled examples, which allows it to learn the patterns and relationships between the input and output sequences.
The Sequence-to-Sequence algorithm is a supervised learning algorithm that requires a large amount of labeled data to train effectively. In the case of a voice recognition system, this means that the algorithm requires a large dataset of recorded voice commands and their corresponding text transcriptions. This data can be obtained by recording the voice commands of customers and manually transcribing them or by using existing speech recognition datasets.
In conclusion, the Sequence-to-Sequence algorithm is the best choice of SageMaker built-in algorithm for implementing the machine learning model behind the voice recognition system for this problem. It is well suited for natural language processing, can model sequences of arbitrary length, and is based on a deep neural network that can learn the mapping between the input and output sequences.