`nemo.collections.asr.models.EncDecSpeakerLabelModel.verify_speakers()` excessive embeddings normalization
**Describe the bug**

`nemo.collections.asr.models.EncDecSpeakerLabelModel.verify_speakers` has excessive embeddings normalization. First computed ambeddings are normalized here:
```
        # Length Normalize
        X = embs1 / torch.linalg.norm(embs1)
        Y = embs2 / torch.linalg.norm(embs2)
```
(https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/models/label_models.py#L510)
Then during similarity computing the dot product is divided by `X` and `Y` norms (which are mathematically identical to `1`; computationally very close to `1`):
```
        similarity_score = torch.dot(X, Y) / ((torch.dot(X, X) * torch.dot(Y, Y)) ** 0.5)
```
(https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/models/label_models.py#L513)

**Steps/Code to reproduce bug**

Just use `verify_speakers()`. The result is accurate, however doing something excessive doesn't look good.


**Expected behavior**

Don't normalize embeddings twice. Either normalize them first, or divide the dot product of (not normalized) embeddings by the norms. No sense to do both.

**Environment overview (please complete the following information)**

 - Environment location: [Bare-metal]
 - Method of NeMo install: `pip install nemo-toolkit[asr]`.

**Environment details**

If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS: Ubuntu 22.04
- PyTorch version: 2.0.1
- Python version: 3.10.12
